analyses

Descriptives statistics, distributions, correlations, and reliability estimates

Driving simulation metrics

Overall

	collisions_overall	speed_overall	distance_overall
speed_overall	-0.04
distance_overall	0.24	0.04
distance_overall_deviation	0.24	0.04	1.00***

## Some items ( 2 4 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' optionSome items ( 2 4 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option

## [[1]]
## [1] "collisions = 0.83"
## 
## [[2]]
## [1] "speed = 0.88"
## 
## [[3]]
## [1] "distance = 0.17"
## 
## [[4]]
## [1] "distance_deviation = 0.17"

Fog-free periods

	collisions_no_fog_overall	speed_no_fog_overall
speed_no_fog_overall	0.34*
distance_no_fog_overall	0.24	-0.03

## Some items ( 1 5 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option

## [[1]]
## [1] "collisions_no_fog = 0.81"
## 
## [[2]]
## [1] "speed_no_fog = 0.75"
## 
## [[3]]
## [1] "distance_no_fog = 0.04"

Fog event probe

	collisions_fog_overall	speed_fog_overall
speed_fog_overall	-0.05
distance_fog_overall	0.27*	0.08

## Some items ( 2 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option

## [[1]]
## [1] "collisions_fog = 0.66"
## 
## [[2]]
## [1] "speed_fog = 0.77"
## 
## [[3]]
## [1] "distance_fog = -0.05"

Psychometric measures

Overall

var	mean_overall	sd_overall	mean_driver	sd_driver	mean_co	sd_co
agreeableness	3.9189815	0.6319277	3.98	0.63	3.86	0.63
bias	3.0441277	20.5145140	3.12	19.66	2.96	21.52
confidence	60.1323343	19.0227797	58.37	19.54	61.89	18.51
congruent_errors	0.9345794	1.3823075	0.93	1.55	0.94	1.20
congruent_time	461.5544460	51.0173810	448.12	50.44	475.24	48.31
conscientiousness	3.2384259	0.7499099	3.10	0.76	3.38	0.72
discrimination	27.2903277	19.4473443	28.85	19.66	25.74	19.29
driving_years	2.7171296	4.9146936	3.28	4.75	2.16	5.06
extraversion	3.1759259	0.7138968	3.18	0.73	3.17	0.70
gaming_time	1.1231481	1.0136320	1.26	1.05	0.99	0.96
gf_accuracy	57.0882066	21.9362721	55.25	21.92	58.93	22.00
incongruent_errors	1.5420561	1.8185645	1.63	1.78	1.45	1.87
incongruent_time	532.1606055	59.6359822	514.92	62.99	549.72	50.82
inhibitory_cost	70.6061595	30.0274495	66.80	31.09	74.49	28.68
intellect	3.6018519	0.6654324	3.56	0.65	3.64	0.69
neuroticism	2.7962963	0.7323852	2.82	0.75	2.77	0.72
repeat_errors	1.6574074	1.7356193	1.78	1.66	1.54	1.82
repeat_time	920.7157111	244.1991035	930.65	241.58	910.78	248.65
resilience	3.6137500	0.3568200	3.64	0.33	3.59	0.38
switch_cost	125.2960244	144.8566483	103.81	116.58	146.79	166.82
switch_errors	2.0370370	2.1352665	2.11	2.00	1.96	2.28
switch_time	1046.0117355	304.7873181	1034.45	289.04	1057.57	322.07
wm_accuracy	43.2716049	18.5563131	40.86	19.43	45.68	17.49

	agreeableness	bias	confidence	congruent_errors	congruent_time	conscientiousness	discrimination	driving_years	extraversion	gaming_time	gf_accuracy	incongruent_errors	incongruent_time	inhibitory_cost	intellect	neuroticism	repeat_errors	repeat_time	resilience	switch_cost	switch_errors	switch_time
bias	-0.07
confidence	0.01	0.39***
congruent_errors	0.11	0.18	0.03
congruent_time	0.03	-0.18	-0.48***	-0.11
conscientiousness	0.06	0.09	-0.05	-0.06	0.17
discrimination	-0.07	-0.35***	0.03	-0.09	-0.17	-0.01
driving_years	0.05	-0.29**	-0.25*	-0.09	0.28**	0.13	-0.04
extraversion	-0.03	0.09	-0.03	-0.10	0.10	-0.04	-0.06	0.05
gaming_time	0.16	-0.07	0.22*	-0.16	-0.17	-0.22*	0.24*	-0.17	-0.18
gf_accuracy	0.07	-0.60***	0.51***	-0.14	-0.24*	-0.12	0.36***	0.06	-0.11	0.26**
incongruent_errors	0.04	0.14	0.14	0.26**	-0.52***	-0.19	0.08	-0.19	-0.04	0.07	-0.01
incongruent_time	-0.03	-0.19	-0.47***	-0.21*	0.86***	0.14	-0.13	0.26**	0.03	-0.17	-0.23*	-0.41***
inhibitory_cost	-0.11	-0.07	-0.11	-0.23*	0.02	-0.02	0.01	0.04	-0.11	-0.04	-0.03	0.07	0.52***
intellect	-0.06	-0.05	0.20*	0.07	-0.10	-0.17	0.14	-0.04	-0.06	0.29**	0.21*	0.07	-0.09	0.00
neuroticism	0.04	-0.12	0.02	0.08	0.03	0.02	0.09	-0.04	0.00	-0.10	0.13	0.12	0.05	0.04	0.07
repeat_errors	-0.11	0.16	-0.13	0.22*	-0.15	-0.24*	-0.16	-0.13	-0.09	-0.15	-0.27**	0.43***	-0.09	0.08	-0.02	0.03
repeat_time	-0.02	-0.09	-0.37***	-0.14	0.51***	0.10	-0.10	0.15	0.10	-0.14	-0.24*	-0.25*	0.41***	-0.05	-0.03	0.17	-0.08
resilience	0.08	-0.02	0.06	-0.12	-0.09	0.14	0.05	0.06	0.17	-0.04	0.07	0.08	-0.12	-0.08	-0.10	-0.37***	-0.01	-0.15
switch_cost	-0.17	-0.29**	-0.25**	-0.10	0.38***	-0.02	0.08	0.20*	0.09	-0.13	0.06	-0.25**	0.32***	-0.01	-0.14	-0.08	-0.11	0.17	0.08
switch_errors	-0.03	0.30**	-0.18	0.25**	-0.01	-0.18	-0.15	-0.13	-0.02	-0.10	-0.44***	0.30**	0.02	0.06	-0.01	0.02	0.58***	-0.12	-0.07	-0.13
switch_time	-0.10	-0.21*	-0.42***	-0.16	0.59***	0.07	-0.04	0.21*	0.13	-0.17	-0.16	-0.32***	0.48***	-0.04	-0.09	0.10	-0.12	0.88***	-0.09	0.61***	-0.16
wm_accuracy	-0.03	0.04	0.36***	-0.08	-0.15	0.02	0.05	-0.13	-0.09	0.14	0.27**	0.07	-0.14	-0.03	0.35***	0.10	-0.07	-0.14	0.06	0.07	-0.11	-0.08

Reliability overall

## [1] "resilience = 0.79"

## [1] "extraversion = 0.7"
## [1] "agreeableness = 0.59"
## [1] "conscientiousness = 0.65"
## [1] "neuroticism = 0.68"
## [1] "intellect = 0.61"

## [[1]]
## [1] "extraversion = 0.7"
## 
## [[2]]
## [1] "agreeableness = 0.59"
## 
## [[3]]
## [1] "conscientiousness = 0.65"
## 
## [[4]]
## [1] "neuroticism = 0.68"
## 
## [[5]]
## [1] "intellect = 0.61"

## [1] "RAPM accuracy = 0.83"

## [1] "RAPM confidence = 0.93"

## [1] "RAPM bias = 0.85"

## [1] "RAPM discrimination = 0.59"

## Some items ( 14 16 19 24 25 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "repeat errors = 0.49"
## Some items ( 1 2 6 7 10 11 12 14 16 18 20 22 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "switch errors = 0.39"

## [[1]]
## [1] "repeat errors = 0.49"
## 
## [[2]]
## [1] "switch errors = 0.39"

## [1] "repeat time = 0.84"
## [1] "switch time = 0.87"

## [[1]]
## [1] "repeat time = 0.84"
## 
## [[2]]
## [1] "switch time = 0.87"

## [1] "switch cost = 0.01"

## [1] "working memory accuracy = 0.7"

## Some items ( 1 4 6 7 8 9 11 12 13 14 15 16 17 19 23 25 28 30 31 32 34 40 44 45 46 49 55 56 60 63 67 71 72 79 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "congruent errors = 0.53"
## Some items ( 20 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "incongruent errors = 0.59"

## [[1]]
## [1] "congruent errors = 0.53"
## 
## [[2]]
## [1] "incongruent errors = 0.59"

## [1] "congruent time = 0.98"
## [1] "incongruent time = 0.82"

## [[1]]
## [1] "congruent time = 0.98"
## 
## [[2]]
## [1] "incongruent time = 0.82"

## [1] "inhibitory cost = 0.95"

Reliability for driver

## Some items ( 18 20 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "resilience = 0.77"

## [1] "extraversion = 0.76"
## [1] "agreeableness = 0.64"
## [1] "conscientiousness = 0.69"
## [1] "neuroticism = 0.68"
## [1] "intellect = 0.59"

## [[1]]
## [1] "extraversion = 0.76"
## 
## [[2]]
## [1] "agreeableness = 0.64"
## 
## [[3]]
## [1] "conscientiousness = 0.69"
## 
## [[4]]
## [1] "neuroticism = 0.68"
## 
## [[5]]
## [1] "intellect = 0.59"

## [1] "RAPM accuracy = 0.83"

## [1] "RAPM confidence = 0.94"

## [1] "RAPM bias = 0.84"

## [1] "RAPM discrimination = 0.45"

## Some items ( 2 3 4 8 9 13 14 17 19 21 23 24 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "switch errors = 0.3"
## Some items ( 2 3 4 5 7 8 9 10 11 12 13 14 19 21 22 26 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "repeat errors = 0.26"

## [[1]]
## [1] "switch errors = 0.3"
## 
## [[2]]
## [1] "repeat errors = 0.26"

## [1] "switch time = 0.88"
## [1] "repeat time = 0.86"

## [[1]]
## [1] "switch time = 0.88"
## 
## [[2]]
## [1] "repeat time = 0.86"

## [1] "switch cost = -0.21"

## Some items ( 15 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "working memory accuracy = 0.73"

## Some items ( 7 8 9 11 12 13 14 16 22 23 28 30 32 34 44 45 56 67 72 79 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "congruent errors = 0.64"
## Some items ( 1 6 11 17 20 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "incongruent errors = 0.56"

## [[1]]
## [1] "congruent errors = 0.64"
## 
## [[2]]
## [1] "incongruent errors = 0.56"

## [1] "congruent time = 0.99"
## [1] "incongruent time = 0.86"

## [[1]]
## [1] "congruent time = 0.99"
## 
## [[2]]
## [1] "incongruent time = 0.86"

## [1] "inhibitory cost = 0.97"

Reliability for codriver

## [1] "resilience = 0.82"

## [1] "extraversion = 0.65"
## [1] "agreeableness = 0.54"
## [1] "conscientiousness = 0.59"
## [1] "neuroticism = 0.68"
## [1] "intellect = 0.63"

## [[1]]
## [1] "extraversion = 0.65"
## 
## [[2]]
## [1] "agreeableness = 0.54"
## 
## [[3]]
## [1] "conscientiousness = 0.59"
## 
## [[4]]
## [1] "neuroticism = 0.68"
## 
## [[5]]
## [1] "intellect = 0.63"

## [1] "RAPM accuracy = 0.84"

## [1] "RAPM confidence = 0.92"

## [1] "RAPM bias = 0.85"

## [1] "RAPM discrimination = 0.72"

## Some items ( 6 9 10 11 12 14 16 17 18 19 24 25 26 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "repeat errors = 0.62"
## Some items ( 3 4 5 8 10 12 13 15 16 17 19 20 21 22 23 24 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "switch errors = 0.48"

## [[1]]
## [1] "repeat errors = 0.62"
## 
## [[2]]
## [1] "switch errors = 0.48"

## [1] "repeat time = 0.82"
## [1] "switch time = 0.87"

## [[1]]
## [1] "repeat time = 0.82"
## 
## [[2]]
## [1] "switch time = 0.87"

## [1] "switch cost = 0.09"

## [1] "working memory accuracy = 0.66"

## Some items ( 1 3 4 6 9 11 12 14 15 17 19 22 23 24 26 31 38 41 42 49 55 57 59 60 71 72 75 76 77 79 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "congruent errors = 0.36"
## Some items ( 2 11 19 20 ) were negatively correlated with the total scale and 
## probably should be reversed.  
## To do this, run the function again with the 'check.keys=TRUE' option[1] "incongruent errors = 0.64"

## [[1]]
## [1] "congruent errors = 0.36"
## 
## [[2]]
## [1] "incongruent errors = 0.64"

## [1] "congruent time = 0.95"
## [1] "incongruent time = 0.74"

## [[1]]
## [1] "congruent time = 0.95"
## 
## [[2]]
## [1] "incongruent time = 0.74"

## [1] "inhibitory cost = 0.93"

Communication measures

	co_info_help_overall	co_info_harm_overall	co_instruct_help_overall	co_instruct_harm_overall	co_redundant_overall	co_question_overall	drive_question_overall	drive_informs_overall
co_info_harm_overall	0.31*
co_instruct_help_overall	0.41**	0.03
co_instruct_harm_overall	0.13	0.43**	0.48***
co_redundant_overall	0.11	0.36**	0.25	0.30*
co_question_overall	0.38**	0.03	0.49***	0.45***	0.16
drive_question_overall	0.63***	0.23	0.41**	0.15	0.21	0.31*
drive_informs_overall	0.52***	0.17	0.45***	0.35**	0.20	0.72***	0.38**
drive_frust_overall	0.14	0.39**	0.30*	0.30*	0.33*	0.11	0.28*	0.26

Reliability estimates

## [[1]]
## [1] "co_info_help = 0.84"
## 
## [[2]]
## [1] "co_info_harm = 0.73"
## 
## [[3]]
## [1] "co_instruct_help = 0.9"
## 
## [[4]]
## [1] "co_instruct_harm = 0.52"
## 
## [[5]]
## [1] "co_redundant = 0.73"
## 
## [[6]]
## [1] "co_question = 0.8"
## 
## [[7]]
## [1] "drive_question = 0.8"
## 
## [[8]]
## [1] "drive_informs = 0.85"
## 
## [[9]]
## [1] "drive_frust = 0.89"

Given the moderate to high correlations between the communication variables, it looks like there is a multicollinearity issue. Need to reduce comms variables to a smaller number using factor analysis.

Conduct EFA to extract comms factors

Reduce variables to smaller number of factors and correlate with each other. It looks like 3 components can be extracted from the comms variables.

Correlations between original comms variables

	co_info_help_overall	co_info_harm_overall	co_instruct_help_overall	co_instruct_harm_overall	co_redundant_overall	co_question_overall	drive_question_overall	drive_informs_overall
co_info_harm_overall	0.31*
co_instruct_help_overall	0.41**	0.03
co_instruct_harm_overall	0.13	0.43**	0.48***
co_redundant_overall	0.11	0.36**	0.25	0.30*
co_question_overall	0.38**	0.03	0.49***	0.45***	0.16
drive_question_overall	0.63***	0.23	0.41**	0.15	0.21	0.31*
drive_informs_overall	0.52***	0.17	0.45***	0.35**	0.20	0.72***	0.38**
drive_frust_overall	0.14	0.39**	0.30*	0.30*	0.33*	0.11	0.28*	0.26

KMO and Bartlett’s test of Spherecity

## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = cor(pca, use = "complete.obs"))
## Overall MSA =  0.65
## MSA for each item = 
##     co_info_help_overall     co_info_harm_overall co_instruct_help_overall 
##                     0.61                     0.44                     0.67 
## co_instruct_harm_overall     co_redundant_overall      co_question_overall 
##                     0.58                     0.78                     0.69 
##   drive_question_overall    drive_informs_overall      drive_frust_overall 
##                     0.77                     0.74                     0.67

## [1] "Bartletts test of spherecity"

##      chisq      p.value df
## 1 176.0047 2.388931e-20 36

Communalities

	communalities
co_info_help_overall	0.82
co_info_harm_overall	0.70
co_instruct_help_overall	0.59
co_instruct_harm_overall	0.74
co_redundant_overall	0.49
co_question_overall	0.81
drive_question_overall	0.76
drive_informs_overall	0.70
drive_frust_overall	0.52

Variance explained by the extracted components

component	eigen	prop_var	cum_var	rotation_SS_load
1	3.54	0.27	0.27	2.42
2	1.47	0.22	0.49	1.98
3	1.12	0.19	0.68	1.74
4	0.78
5	0.68
6	0.62
7	0.37
8	0.22
9	0.19

Pattern matrix

var	PC1	PC2	PC3
co_question_overall	0.95
drive_informs_overall	0.73
co_instruct_help_overall	0.71
co_instruct_harm_overall	0.62	0.46	-0.33
co_info_harm_overall		0.87
drive_frust_overall		0.71
co_redundant_overall		0.68
co_info_help_overall			0.85
drive_question_overall			0.81

Component correlations matrix

##              inconsistent terrible helpful
## inconsistent         1.00     0.38    0.33
## terrible             0.38     1.00    0.17
## helpful              0.33     0.17    1.00

Check that component scores extracted using SPSS and R are the same

##               component      r
## 1 inconsistent_codriver 0.9998
## 2     terrible_codriver 0.9996
## 3      helpful_exchange 0.9997

Regression analyses

Correlations between all variables

	collisions_overall	speed_overall	distance_overall	inconsistent_codriver	terrible_codriver	helpful_exchange	age_co_driver	age_driver	aus_born_co_driver	aus_born_driver	eng_fl_co_driver	eng_fl_driver	sex_co_driver	sex_driver	prop_female	driving_years	gaming_time	congruent_errors	congruent_time	incongruent_errors	incongruent_time	inhibitory_cost	repeat_errors	repeat_time	switch_errors	switch_time	switch_cost	wm_accuracy	resilience	gf_accuracy	confidence	bias	discrimination	agreeableness	conscientiousness	extraversion	intellect	neuroticism	driving_years_drone	gaming_time_drone	congruent_errors_drone	congruent_time_drone	incongruent_errors_drone	incongruent_time_drone	inhibitory_cost_drone	repeat_errors_drone	repeat_time_drone	switch_errors_drone	switch_time_drone	switch_cost_drone	wm_accuracy_drone	resilience_drone	gf_accuracy_drone	confidence_drone	bias_drone	discrimination_drone	agreeableness_drone	conscientiousness_drone	extraversion_drone	intellect_drone
speed_overall	-0.04
distance_overall	0.24	0.04
inconsistent_codriver	0.28*	-0.27*	0.27*
terrible_codriver	0.32*	-0.09	0.38**	0.34*
helpful_exchange	0.07	-0.40**	0.20	0.32*	0.17
age_co_driver	0.03	0.10	0.19	-0.02	-0.05	0.12
age_driver	-0.01	-0.04	0.00	-0.11	0.07	0.20	-0.08
aus_born_co_driver	-0.05	-0.09	0.07	0.20	-0.17	0.30*	-0.12	0.02
aus_born_driver	-0.03	0.01	-0.09	0.04	0.02	0.00	0.14	-0.27*	-0.03
eng_fl_co_driver	0.01	-0.22	0.11	0.18	-0.31*	0.30*	0.06	0.10	0.53***	0.01
eng_fl_driver	-0.27	0.15	-0.10	-0.17	-0.23	-0.03	0.10	-0.19	0.09	0.40**	0.11
sex_co_driver	-0.22	0.08	-0.08	0.06	-0.04	0.05	-0.11	-0.07	0.07	0.14	-0.04	0.09
sex_driver	-0.28*	0.58***	0.07	-0.21	-0.13	-0.24	0.11	-0.19	0.06	0.00	-0.20	0.04	0.22
prop_female	0.32*	-0.47***	0.00	0.13	0.12	0.15	-0.02	0.18	-0.08	-0.07	0.16	-0.07	-0.70***	-0.85***
driving_years	-0.08	-0.06	-0.03	-0.10	0.03	0.19	-0.07	0.95***	-0.03	-0.25	0.16	-0.07	-0.04	-0.26	0.22
gaming_time	0.12	-0.01	0.24	0.05	0.09	0.19	-0.07	-0.05	0.08	-0.08	-0.13	-0.39**	0.15	0.29*	-0.29*	-0.16
congruent_errors	0.07	-0.13	-0.22	-0.17	-0.22	-0.18	0.00	-0.19	0.03	0.06	0.15	0.19	-0.08	-0.04	0.07	-0.17	-0.20
congruent_time	0.04	-0.11	-0.18	0.01	0.00	0.08	-0.24	0.28*	0.10	-0.04	0.09	-0.09	-0.09	-0.36**	0.31*	0.32*	-0.29*	-0.11
incongruent_errors	0.37**	-0.01	0.34*	0.10	-0.01	0.03	0.42**	-0.20	0.00	0.00	0.12	0.01	0.01	0.08	-0.06	-0.23	0.12	0.26	-0.47***
incongruent_time	0.15	-0.03	-0.15	0.02	0.03	0.05	-0.17	0.25	0.09	-0.01	0.02	-0.07	-0.07	-0.36**	0.30*	0.28*	-0.25	-0.22	0.87***	-0.38**
inhibitory_cost	0.24	0.11	0.00	0.01	0.06	-0.02	0.04	0.05	0.02	0.05	-0.11	0.00	0.01	-0.14	0.09	0.06	-0.03	-0.27	0.15	0.00	0.61***
repeat_errors	0.21	-0.04	0.11	0.10	0.08	0.06	0.33*	-0.21	-0.20	0.00	0.04	0.05	0.06	0.01	-0.04	-0.19	-0.19	0.21	-0.07	0.47***	-0.03	0.04
repeat_time	0.09	-0.05	-0.04	0.15	0.14	0.06	-0.18	0.09	0.10	0.08	0.02	-0.05	0.04	-0.21	0.13	0.14	-0.20	-0.13	0.62***	-0.28*	0.48***	-0.03	-0.10
switch_errors	0.24	-0.05	-0.04	-0.12	0.00	-0.23	0.09	-0.25	-0.26	0.23	0.04	0.24	-0.08	-0.19	0.18	-0.22	-0.31*	0.36**	0.06	0.27	0.02	-0.07	0.43**	-0.06
switch_time	0.02	0.00	-0.17	0.10	0.13	0.01	-0.17	0.16	0.02	0.02	-0.11	-0.05	-0.02	-0.13	0.11	0.20	-0.24	-0.21	0.70***	-0.39**	0.57***	0.01	-0.14	0.92***	-0.08
switch_cost	-0.14	0.11	-0.34*	-0.07	0.04	-0.09	-0.04	0.20	-0.14	-0.11	-0.30*	-0.03	-0.12	0.10	-0.01	0.21	-0.18	-0.25	0.45***	-0.38**	0.41**	0.10	-0.13	0.21	-0.06	0.58***
wm_accuracy	-0.12	-0.06	0.11	0.17	0.10	0.03	-0.13	0.02	0.06	-0.24	0.09	-0.04	0.15	0.13	-0.18	0.02	0.18	-0.11	-0.15	0.03	-0.08	0.08	-0.11	-0.24	-0.05	-0.19	0.03
resilience	0.02	0.20	-0.03	0.16	0.24	0.11	-0.03	0.19	-0.20	-0.21	-0.09	-0.39**	-0.03	0.22	-0.15	0.13	0.00	-0.14	-0.05	0.09	-0.08	-0.08	0.25	-0.21	-0.09	-0.09	0.21	0.26
gf_accuracy	-0.14	0.15	0.16	0.12	0.11	0.10	0.09	0.20	0.03	-0.19	-0.07	-0.20	0.09	0.34*	-0.29*	0.20	0.40**	-0.25	-0.32*	-0.13	-0.21	0.10	-0.26	-0.18	-0.45***	-0.15	0.02	0.30*	0.12
confidence	-0.10	0.06	0.20	0.14	0.10	-0.05	0.14	-0.19	-0.21	-0.03	-0.10	-0.14	-0.06	0.20	-0.12	-0.22	0.38**	-0.08	-0.57***	0.00	-0.49***	-0.07	-0.21	-0.32*	-0.18	-0.39**	-0.29*	0.35*	0.02	0.56***
bias	0.06	-0.11	0.03	0.00	-0.02	-0.16	0.05	-0.42**	-0.24	0.18	-0.03	0.09	-0.15	-0.17	0.21	-0.44***	-0.07	0.20	-0.21	0.14	-0.26	-0.18	0.08	-0.12	0.32*	-0.22	-0.31*	0.01	-0.10	-0.56***	0.37**
discrimination	-0.06	0.21	0.08	-0.14	-0.13	-0.06	0.07	-0.10	0.01	0.10	-0.17	-0.06	0.34*	0.38**	-0.46***	-0.15	0.32*	-0.14	-0.22	0.13	-0.20	-0.04	-0.13	-0.02	-0.06	-0.01	0.01	0.10	-0.05	0.32*	0.11	-0.25
agreeableness	0.12	-0.33*	0.05	0.33*	0.15	0.45***	-0.15	0.13	0.32*	0.10	0.37**	-0.05	-0.01	-0.29*	0.22	0.14	0.13	0.15	0.06	0.06	0.01	-0.07	-0.07	0.07	0.03	-0.04	-0.24	0.06	0.08	0.17	0.04	-0.15	-0.04
conscientiousness	-0.15	-0.07	0.05	0.02	0.05	0.02	-0.21	0.15	0.05	0.05	0.09	-0.09	-0.08	-0.03	0.07	0.15	-0.20	0.09	0.19	-0.11	0.13	-0.05	-0.15	0.06	0.00	0.04	-0.03	0.03	0.24	-0.13	-0.15	0.00	0.00	0.28*
extraversion	-0.16	0.00	-0.08	0.12	-0.11	0.17	0.11	-0.01	-0.03	0.23	-0.10	0.23	-0.03	-0.19	0.15	0.07	-0.33*	-0.17	0.12	-0.03	0.06	-0.07	-0.05	0.26	0.03	0.26	0.11	-0.15	-0.04	-0.23	-0.19	0.07	0.09	-0.11	-0.08
intellect	0.04	0.04	-0.08	-0.22	0.03	-0.11	-0.06	0.01	0.11	-0.01	0.01	0.08	0.05	0.06	-0.07	0.01	0.23	0.28*	-0.17	0.05	-0.10	0.07	-0.08	-0.12	0.05	-0.15	-0.13	0.40**	-0.02	0.19	0.19	-0.02	0.11	0.07	-0.28*	-0.24
neuroticism	0.12	0.04	0.26	-0.05	-0.12	-0.18	0.09	-0.12	-0.02	-0.08	0.17	0.08	-0.18	-0.05	0.13	-0.04	-0.24	0.16	0.00	0.18	0.05	0.11	0.07	0.14	0.22	0.00	-0.28*	-0.03	-0.34*	0.02	-0.03	-0.06	0.09	-0.04	0.06	0.07	0.00
driving_years_drone	0.05	0.11	0.21	0.03	-0.12	0.11	0.95***	-0.07	-0.07	0.09	0.18	0.09	-0.06	0.15	-0.07	-0.09	-0.07	0.03	-0.25	0.43**	-0.19	0.02	0.32*	-0.20	0.07	-0.21	-0.11	-0.12	0.08	0.07	0.14	0.06	0.12	-0.07	-0.15	0.06	-0.09	0.10
gaming_time_drone	-0.05	0.00	-0.05	-0.10	-0.03	-0.05	-0.19	0.21	0.07	0.05	-0.08	-0.03	0.32*	0.04	-0.20	0.20	0.21	-0.02	-0.21	-0.01	-0.11	0.12	0.04	-0.14	0.02	-0.22	-0.26	0.12	0.04	0.13	-0.04	-0.19	0.04	0.13	-0.12	0.01	0.24	0.03	-0.22
congruent_errors_drone	-0.06	0.07	0.02	-0.09	-0.18	0.07	-0.02	0.13	0.12	-0.18	0.08	0.23	0.02	0.08	-0.07	0.21	-0.07	0.07	0.07	0.01	0.06	0.01	-0.22	0.03	-0.31*	0.03	0.02	0.13	-0.16	-0.01	-0.07	-0.06	0.14	-0.18	0.05	0.14	0.05	0.08	0.00	-0.12
congruent_time_drone	-0.06	-0.03	-0.06	-0.12	-0.26	-0.14	0.32*	-0.06	-0.08	0.14	0.22	0.00	-0.19	0.00	0.10	-0.06	-0.03	0.20	-0.24	0.23	-0.18	0.02	0.11	-0.20	0.21	-0.25	-0.19	0.04	-0.09	-0.01	0.17	0.17	-0.19	-0.02	-0.02	-0.03	0.03	-0.02	0.34*	0.02	-0.12
incongruent_errors_drone	-0.07	0.04	0.07	0.06	0.11	0.01	-0.14	-0.06	-0.11	0.06	-0.21	0.14	0.30*	0.11	-0.24	0.01	0.09	0.02	0.16	-0.08	0.05	-0.15	-0.03	0.14	-0.02	0.11	-0.02	-0.09	-0.11	0.14	-0.02	-0.17	0.26	0.04	0.01	-0.10	0.06	0.16	-0.16	0.00	0.27	-0.59***
incongruent_time_drone	0.01	-0.07	-0.02	-0.12	-0.28*	-0.14	0.32*	-0.10	0.00	0.07	0.21	-0.03	-0.23	0.05	0.09	-0.13	0.04	0.40**	-0.31*	0.38**	-0.30*	-0.10	0.06	-0.25	0.23	-0.32*	-0.27*	0.09	0.06	0.04	0.17	0.12	-0.11	0.17	0.10	-0.13	0.14	0.03	0.35*	0.01	-0.22	0.83***	-0.47***
inhibitory_cost_drone	0.11	-0.07	0.06	-0.01	-0.05	-0.02	0.03	-0.08	0.13	-0.12	0.00	-0.05	-0.09	0.08	-0.01	-0.14	0.11	0.37**	-0.15	0.29*	-0.23	-0.22	-0.08	-0.10	0.06	-0.15	-0.17	0.10	0.24	0.09	0.03	-0.07	0.12	0.34*	0.22	-0.18	0.21	0.09	0.05	-0.02	-0.19	-0.21	0.16	0.37**
repeat_errors_drone	0.00	0.01	0.06	0.17	0.25	0.04	-0.12	-0.03	0.04	0.07	-0.05	0.14	0.06	0.11	-0.11	-0.01	-0.05	0.21	-0.02	0.13	-0.06	-0.08	0.09	0.04	-0.04	-0.01	-0.10	0.14	0.17	0.13	-0.08	-0.23	0.19	0.20	0.27*	-0.05	0.10	0.27	-0.09	-0.13	0.23	-0.20	0.39**	-0.11	0.13
repeat_time_drone	0.01	0.26	0.13	-0.07	-0.02	0.03	0.23	-0.08	-0.15	-0.07	0.01	0.01	-0.13	0.28*	-0.13	-0.07	0.04	-0.01	-0.21	0.23	-0.19	-0.04	0.25	-0.08	0.16	-0.11	-0.10	0.05	0.19	0.14	0.19	0.03	-0.26	-0.12	-0.05	-0.08	-0.04	0.05	0.15	-0.09	-0.16	0.46***	-0.22	0.41**	-0.05	-0.07
switch_errors_drone	-0.21	-0.15	0.17	0.19	0.07	0.03	-0.14	0.03	0.20	0.12	0.22	0.22	0.21	0.03	-0.14	0.08	-0.09	0.35*	-0.09	0.15	-0.24	-0.35**	0.06	0.01	0.02	-0.09	-0.25	0.15	0.04	0.10	-0.08	-0.19	0.17	0.31*	0.13	-0.05	0.16	0.29*	-0.06	0.08	0.15	-0.07	0.32*	0.05	0.21	0.70***	-0.17
switch_time_drone	-0.01	0.31*	0.09	0.01	-0.12	0.06	0.28*	-0.06	-0.01	0.05	0.22	0.06	-0.12	0.26	-0.13	-0.04	0.06	-0.05	-0.20	0.27*	-0.15	0.03	0.21	-0.03	0.09	-0.08	-0.13	0.09	0.10	0.19	0.24	0.03	-0.15	0.02	-0.02	-0.02	-0.02	0.04	0.24	-0.10	-0.11	0.51***	-0.25	0.43**	-0.11	-0.10	0.86***	-0.22
switch_cost_drone	-0.04	0.20	-0.01	0.11	-0.21	0.08	0.20	0.00	0.20	0.21	0.42**	0.09	-0.04	0.09	-0.04	0.04	0.07	-0.08	-0.07	0.19	0.00	0.12	0.04	0.07	-0.06	0.01	-0.11	0.10	-0.10	0.16	0.18	0.01	0.10	0.22	0.04	0.08	0.03	0.00	0.23	-0.06	0.03	0.30*	-0.16	0.21	-0.13	-0.09	0.17	-0.17	0.65***
wm_accuracy_drone	-0.05	0.09	-0.12	-0.16	0.12	-0.12	-0.24	-0.02	-0.21	-0.10	-0.19	-0.10	0.18	0.02	-0.11	0.02	0.03	-0.23	0.05	-0.11	0.02	-0.03	-0.05	0.01	-0.01	0.04	0.07	0.15	0.01	0.07	0.08	0.00	-0.03	-0.07	-0.03	-0.06	0.03	-0.02	-0.26	0.13	-0.03	-0.24	0.13	-0.33*	-0.19	-0.01	-0.03	-0.16	0.02	0.07
resilience_drone	-0.04	-0.22	0.18	0.14	0.19	0.28	-0.10	0.15	0.02	0.05	-0.01	-0.30*	0.07	-0.19	0.11	0.10	0.26	-0.24	0.07	-0.16	-0.08	-0.26	-0.26	0.27	-0.28	0.26	0.09	-0.05	-0.11	0.04	0.12	0.07	0.17	0.12	0.06	0.12	-0.08	-0.22	-0.11	-0.11	-0.10	-0.10	0.07	-0.14	-0.07	-0.19	-0.12	-0.07	-0.09	0.01	-0.11
gf_accuracy_drone	0.16	-0.03	-0.01	-0.17	-0.07	-0.07	0.01	-0.19	-0.08	0.14	-0.12	0.09	0.08	-0.15	0.06	-0.13	0.07	-0.18	0.11	-0.03	0.22	0.26	0.03	0.09	0.14	0.09	0.04	-0.19	-0.36*	-0.12	-0.08	0.06	0.09	-0.04	-0.20	0.11	-0.10	-0.07	-0.05	0.14	0.00	-0.23	0.13	-0.33*	-0.20	-0.27*	-0.29*	-0.43**	-0.19	0.06	0.23	0.04
confidence_drone	0.19	-0.05	-0.11	0.12	0.23	-0.09	-0.24	-0.13	-0.16	-0.02	-0.30*	-0.21	0.19	-0.18	0.02	-0.13	-0.04	-0.06	0.24	-0.20	0.24	0.10	-0.12	0.21	-0.03	0.25	0.18	-0.08	-0.07	-0.26	-0.15	0.14	-0.03	-0.12	-0.03	-0.03	-0.06	-0.15	-0.26	0.08	0.19	-0.47***	0.31*	-0.55***	-0.18	-0.05	-0.41**	-0.18	-0.46***	-0.27	0.36**	0.12	0.45***
bias_drone	0.01	-0.02	-0.08	0.28*	0.27*	-0.01	-0.22	0.09	-0.06	-0.16	-0.13	-0.27*	0.08	0.00	-0.04	0.03	-0.10	0.13	0.10	-0.15	-0.01	-0.19	-0.13	0.09	-0.17	0.12	0.11	0.13	0.34*	-0.10	-0.05	0.06	-0.12	-0.06	0.18	-0.14	0.05	-0.05	-0.17	-0.08	0.16	-0.17	0.13	-0.13	0.05	0.23	-0.06	0.29*	-0.20	-0.29*	0.08	0.06	-0.64***	0.40**
discrimination_drone	0.04	-0.07	0.05	-0.19	-0.21	0.01	0.05	0.13	0.21	-0.02	-0.05	0.01	0.09	-0.09	0.01	0.12	0.17	-0.15	0.07	0.15	0.10	0.08	-0.14	0.09	0.00	0.05	-0.07	-0.11	-0.27	-0.08	-0.15	-0.06	0.28*	0.19	0.13	0.12	-0.07	-0.11	0.04	0.14	-0.03	-0.08	0.03	-0.02	0.10	-0.20	-0.20	-0.24	-0.07	0.16	0.01	0.13	0.42**	-0.03	-0.45***
agreeableness_drone	-0.01	0.01	0.06	0.14	0.08	0.03	-0.10	0.11	0.10	-0.25	0.08	-0.07	0.06	0.04	-0.06	0.05	0.21	-0.11	-0.03	-0.10	0.06	0.17	0.08	-0.12	-0.10	-0.11	-0.03	0.12	-0.04	0.10	0.03	-0.08	-0.09	-0.14	-0.13	-0.26	-0.06	-0.16	-0.05	0.17	0.07	0.06	0.02	-0.01	-0.12	-0.16	-0.11	-0.09	-0.14	-0.11	-0.11	0.05	-0.01	-0.01	0.01	-0.12
conscientiousness_drone	0.03	0.09	-0.10	0.03	0.06	0.08	0.12	0.11	-0.17	-0.07	-0.25	-0.12	0.06	0.06	-0.08	0.03	-0.10	-0.30*	0.06	-0.01	0.11	0.14	0.00	-0.06	-0.14	0.08	0.31*	-0.14	0.28	-0.07	-0.10	-0.02	-0.07	-0.19	-0.07	0.05	-0.24	-0.35*	0.15	-0.21	-0.29*	0.06	-0.26	0.03	-0.04	-0.31*	0.16	-0.33*	0.08	-0.07	-0.04	0.08	-0.15	0.03	0.17	0.01	-0.14
extraversion_drone	-0.16	-0.08	0.04	0.15	0.08	0.16	0.04	-0.05	0.06	-0.03	0.09	-0.33*	-0.02	-0.04	0.04	-0.03	0.07	-0.14	0.25	-0.23	0.12	-0.16	0.00	0.13	-0.05	0.19	0.22	0.02	0.23	0.07	-0.01	-0.09	0.15	-0.03	0.07	0.25	-0.12	-0.18	0.03	-0.02	-0.01	0.09	-0.05	0.00	-0.15	-0.14	-0.05	-0.06	0.00	0.09	-0.02	0.35*	0.01	0.16	0.12	-0.23	0.06	0.01
intellect_drone	0.02	-0.08	0.14	-0.11	0.29*	-0.13	-0.02	-0.01	-0.13	-0.01	-0.25	-0.14	0.03	0.00	-0.02	0.00	0.13	-0.18	0.04	-0.05	0.16	0.26	0.11	-0.18	0.18	-0.16	-0.02	0.16	0.07	0.18	0.07	-0.13	-0.08	0.07	0.00	-0.17	0.08	0.01	-0.08	0.38**	-0.19	-0.07	0.09	-0.12	-0.09	0.05	0.06	-0.06	-0.04	-0.17	0.29*	-0.15	0.23	0.20	-0.07	0.18	-0.17	-0.10	0.11
neuroticism_drone	0.05	0.27*	0.13	-0.10	-0.06	-0.26	0.02	0.00	-0.08	-0.03	-0.11	-0.05	-0.15	0.17	-0.04	0.03	0.06	0.04	0.03	0.04	0.11	0.18	0.00	-0.04	0.09	-0.05	-0.03	-0.11	-0.09	0.19	0.09	-0.13	-0.12	-0.10	0.12	-0.24	-0.06	0.15	-0.04	0.04	-0.04	0.10	0.06	0.08	-0.03	-0.01	0.20	-0.16	0.19	0.07	0.26	-0.41**	0.25	0.09	-0.18	0.09	0.11	-0.02	-0.08	0.15

Predicting the communication factors

Which variables sig. correlate with the communication factors?

rowname inconsistent_codriver agreeableness 0.33 bias_drone 0.28

rowname terrible_codriver bias_drone 0.27 eng_fl_co_driver -0.31 incongruent_time_drone -0.28 intellect_drone 0.29

rowname helpful_exchange agreeableness 0.45 aus_born_co_driver 0.30 eng_fl_co_driver 0.30

Inconsistent codriver

## [1] "DV = inconsistent_codriver"
## 
## Call:
## lm(formula = fm, data = var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.63493 -0.58181 -0.01837  0.54393  2.70811 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)   
## (Intercept)          -5.360e-16  1.243e-01   0.000  1.00000   
## scale(agreeableness)  3.448e-01  1.257e-01   2.742  0.00839 **
## scale(bias_drone)     3.026e-01  1.257e-01   2.406  0.01977 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9134 on 51 degrees of freedom
## Multiple R-squared:  0.1971, Adjusted R-squared:  0.1656 
## F-statistic: 6.261 on 2 and 51 DF,  p-value: 0.003704
## 
##               zero_order partial part
## agreeableness       0.33    0.36 0.34
## bias_drone          0.28    0.32 0.30

## 
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.9959         0
## Farrar Chi-Square:         0.2100         0
## Red Indicator:             0.0638         0
## Sum of Lambda Inverse:     2.0082         0
## Theil's Method:           -0.1890         0
## Condition Number:         12.8855         0
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                  VIF    TOL     Wi  Fi Leamer   CVIF Klein   IND1 IND2
## agreeableness 1.0041 0.9959 0.2124 Inf  0.998 0.9888     0 0.0192    1
## bias_drone    1.0041 0.9959 0.2124 Inf  0.998 0.9888     0 0.0192    1
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## * all coefficients have significant t-ratios
## 
## R-square of y on all x: 0.1971 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================

Helpful exchange

## [1] "DV = helpful_exchange"
## 
## Call:
## lm(formula = fm, data = var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7340 -0.5996  0.0668  0.5615  1.7881 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)   
## (Intercept)             -8.728e-17  1.219e-01   0.000  1.00000   
## scale(agreeableness)     3.967e-01  1.323e-01   2.999  0.00418 **
## scale(eng_fl_co_driver)  1.575e-01  1.323e-01   1.191  0.23935   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8956 on 51 degrees of freedom
## Multiple R-squared:  0.2282, Adjusted R-squared:  0.1979 
## F-statistic: 7.538 on 2 and 51 DF,  p-value: 0.001355
## 
##                  zero_order partial part
## agreeableness          0.45    0.39 0.37
## eng_fl_co_driver       0.30    0.16 0.15

## 
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.8647         0
## Farrar Chi-Square:         7.4863         1
## Red Indicator:             0.3678         0
## Sum of Lambda Inverse:     2.3129         0
## Theil's Method:            0.0424         0
## Condition Number:         15.6544         0
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                     VIF    TOL    Wi  Fi Leamer   CVIF Klein   IND1 IND2
## agreeableness    1.1565 0.8647 8.136 Inf 0.9299 1.2729     0 0.0166    1
## eng_fl_co_driver 1.1565 0.8647 8.136 Inf 0.9299 1.2729     0 0.0166    1
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## eng_fl_co_driver , coefficient(s) are non-significant may be due to multicollinearity
## 
## R-square of y on all x: 0.2282 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================

Terrible codriver

## [1] "DV = terrible_codriver"
## 
## Call:
## lm(formula = fm, data = var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4445 -0.4996 -0.0833  0.3611  2.7814 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                   -0.06152    0.11005  -0.559   0.5788  
## scale(bias_drone)              0.25612    0.11237   2.279   0.0271 *
## scale(eng_fl_co_driver)       -0.10096    0.11857  -0.851   0.3987  
## scale(incongruent_time_drone) -0.16625    0.11456  -1.451   0.1532  
## scale(intellect_drone)         0.18504    0.11547   1.603   0.1156  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8007 on 48 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.2261, Adjusted R-squared:  0.1617 
## F-statistic: 3.507 on 4 and 48 DF,  p-value: 0.01367
## 
##                        zero_order partial  part
## bias_drone                   0.32    0.31  0.29
## eng_fl_co_driver            -0.24   -0.12 -0.11
## incongruent_time_drone      -0.28   -0.21 -0.18
## intellect_drone              0.24    0.23  0.20

## 
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.8676         0
## Farrar Chi-Square:         7.0747         0
## Red Indicator:             0.1562         0
## Sum of Lambda Inverse:     4.2872         0
## Theil's Method:           -0.4124         0
## Condition Number:         32.7085         1
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                           VIF    TOL     Wi     Fi Leamer   CVIF Klein   IND1
## bias_drone             1.0433 0.9585 0.7069 1.0820 0.9790 1.1483     0 0.0587
## eng_fl_co_driver       1.1102 0.9008 1.7995 2.7543 0.9491 1.2219     0 0.0551
## incongruent_time_drone 1.0644 0.9395 1.0519 1.6100 0.9693 1.1715     0 0.0575
## intellect_drone        1.0693 0.9352 1.1324 1.7333 0.9670 1.1770     0 0.0573
##                          IND2
## bias_drone             0.6237
## eng_fl_co_driver       1.4920
## incongruent_time_drone 0.9096
## intellect_drone        0.9748
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## eng_fl_co_driver , incongruent_time_drone , intellect_drone , coefficient(s) are non-significant may be due to multicollinearity
## 
## R-square of y on all x: 0.2261 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================

Predicting the driving-simulation metrics

Which variables sig. correlate with the driving-simulation metrics overall?

rowname collisions_overall incongruent_errors 0.37 inconsistent_codriver 0.28 prop_female 0.32 sex_driver -0.28 terrible_codriver 0.32

rowname speed_overall agreeableness -0.33 helpful_exchange -0.40 inconsistent_codriver -0.27 neuroticism_drone 0.27 prop_female -0.47 sex_driver 0.58 switch_time_drone 0.31

rowname distance_overall incongruent_errors 0.34 inconsistent_codriver 0.27 switch_cost -0.34 terrible_codriver 0.38

Collisions overall

Using proportion of females

## [1] "DV = collisions_overall"
## 
## Call:
## lm(formula = fm, data = var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -137.26  -49.20  -13.43   33.23  292.60 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    165.46      12.36  13.384   <2e-16 ***
## scale(incongruent_errors)       40.91      12.59   3.248   0.0021 ** 
## scale(inconsistent_codriver)    13.29      13.40   0.992   0.3261    
## scale(prop_female)              32.52      12.65   2.570   0.0133 *  
## scale(terrible_codriver)        27.36      13.31   2.056   0.0451 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 90.85 on 49 degrees of freedom
## Multiple R-squared:  0.3515, Adjusted R-squared:  0.2985 
## F-statistic: 6.639 on 4 and 49 DF,  p-value: 0.000237
## 
##                       zero_order partial part
## incongruent_errors          0.37    0.42 0.37
## inconsistent_codriver       0.28    0.14 0.11
## prop_female                 0.32    0.34 0.30
## terrible_codriver           0.32    0.28 0.24

## 
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.8516         0
## Farrar Chi-Square:         8.1628         0
## Red Indicator:             0.1618         0
## Sum of Lambda Inverse:     4.3369         0
## Theil's Method:           -0.7556         0
## Condition Number:          4.9025         0
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                          VIF    TOL     Wi     Fi Leamer   CVIF Klein   IND1
## incongruent_errors    1.0185 0.9818 0.3085 0.4720 0.9909 1.1464     0 0.0589
## inconsistent_codriver 1.1533 0.8671 2.5545 3.9084 0.9312 1.2981     0 0.0520
## prop_female           1.0281 0.9726 0.4688 0.7173 0.9862 1.1572     0 0.0584
## terrible_codriver     1.1370 0.8795 2.2830 3.4931 0.9378 1.2798     0 0.0528
##                         IND2
## incongruent_errors    0.2432
## inconsistent_codriver 1.7785
## prop_female           0.3661
## terrible_codriver     1.6122
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## inconsistent_codriver , coefficient(s) are non-significant may be due to multicollinearity
## 
## R-square of y on all x: 0.3515 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================

##                       vars  n mean sd median trimmed  mad   min  max range
## incongruent_errors       1 54    0  1  -0.35   -0.15 0.83 -0.91 3.01  3.92
## inconsistent_codriver    2 54    0  1  -0.05   -0.07 1.24 -1.39 2.49  3.89
## prop_female              3 54    0  1  -0.43    0.10 2.15 -1.88 1.02  2.90
## terrible_codriver        4 54    0  1  -0.15   -0.15 0.73 -1.37 3.60  4.98
##                        skew kurtosis   se
## incongruent_errors     1.12     0.39 0.14
## inconsistent_codriver  0.56    -0.54 0.14
## prop_female           -0.44    -0.91 0.14
## terrible_codriver      1.57     2.88 0.14
##                       vars  n mean sd median trimmed  mad   min  max range
## incongruent_errors       1 54    0  1  -0.35   -0.15 0.83 -0.91 3.01  3.92
## inconsistent_codriver    2 54    0  1  -0.05   -0.07 1.24 -1.39 2.49  3.89
## prop_female              3 54    0  1  -0.43    0.10 2.15 -1.88 1.02  2.90
## terrible_codriver        4 54    0  1  -0.15   -0.15 0.73 -1.37 3.60  4.98
##                        skew kurtosis   se
## incongruent_errors     1.12     0.39 0.14
## inconsistent_codriver  0.56    -0.54 0.14
## prop_female           -0.44    -0.91 0.14
## terrible_codriver      1.57     2.88 0.14

## [[1]]
## 
## Call:
## lm(formula = fm, data = var_std)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -142.87  -46.20  -14.95   31.50  290.29 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                        165.054     12.583  13.118  < 2e-16 ***
## inconsistent_codriver               12.771     13.681   0.933  0.35524    
## prop_female                         32.188     12.840   2.507  0.01562 *  
## incongruent_errors                  40.731     12.734   3.199  0.00245 ** 
## terrible_codriver                   27.239     13.444   2.026  0.04832 *  
## inconsistent_codriver:prop_female    3.322     12.887   0.258  0.79765    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 91.73 on 48 degrees of freedom
## Multiple R-squared:  0.3524, Adjusted R-squared:  0.2849 
## F-statistic: 5.224 on 5 and 48 DF,  p-value: 0.0006588
## 
## 
## [[2]]
## 
## Call:
## lm(formula = fm, data = var_std)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -135.24  -55.10  -12.84   34.41  291.74 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    166.559     12.594  13.225  < 2e-16 ***
## terrible_codriver               29.824     14.069   2.120  0.03922 *  
## prop_female                     32.376     12.743   2.541  0.01435 *  
## incongruent_errors              41.449     12.717   3.259  0.00206 ** 
## inconsistent_codriver           13.386     13.495   0.992  0.32621    
## terrible_codriver:prop_female   -9.455     16.497  -0.573  0.56922    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 91.48 on 48 degrees of freedom
## Multiple R-squared:  0.3559, Adjusted R-squared:  0.2888 
## F-statistic: 5.304 on 5 and 48 DF,  p-value: 0.0005859
## 
## 
## [[3]]
## NULL

Using sex of driver

## [1] "DV = collisions_overall"
##                       vars  n mean  sd median trimmed  mad   min  max range
## incongruent_errors       1 54 0.00 1.0  -0.35   -0.15 0.83 -0.91 3.01  3.92
## inconsistent_codriver    2 54 0.00 1.0  -0.05   -0.07 1.24 -1.39 2.49  3.89
## sex_driver*              3 54 1.54 0.5   2.00    1.55 0.00  1.00 2.00  1.00
## terrible_codriver        4 54 0.00 1.0  -0.15   -0.15 0.73 -1.37 3.60  4.98
##                        skew kurtosis   se
## incongruent_errors     1.12     0.39 0.14
## inconsistent_codriver  0.56    -0.54 0.14
## sex_driver*           -0.14    -2.02 0.07
## terrible_codriver      1.57     2.88 0.14
## 
## Call:
## lm(formula = fm, data = std_var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -134.27  -51.65  -14.83   37.86  298.22 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    194.74      18.86  10.326 6.87e-14 ***
## scale(incongruent_errors)       41.27      12.89   3.202  0.00239 ** 
## scale(inconsistent_codriver)    11.08      13.88   0.799  0.42835    
## scale(terrible_codriver)        28.42      13.56   2.095  0.04135 *  
## sex_driver1                    -54.51      26.10  -2.089  0.04195 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 92.74 on 49 degrees of freedom
## Multiple R-squared:  0.3242, Adjusted R-squared:  0.2691 
## F-statistic: 5.878 on 4 and 49 DF,  p-value: 0.0006046
## 
##                       zero_order partial  part
## incongruent_errors          0.37    0.42  0.38
## inconsistent_codriver       0.28    0.11  0.09
## sex_driver                 -0.28   -0.29 -0.25
## terrible_codriver           0.32    0.29  0.25

## 
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.8236         0
## Farrar Chi-Square:         9.8672         0
## Red Indicator:             0.1791         0
## Sum of Lambda Inverse:     4.4072         0
## Theil's Method:           -0.6150         0
## Condition Number:          3.1933         0
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                          VIF    TOL     Wi     Fi Leamer   CVIF Klein   IND1
## incongruent_errors    1.0236 0.9770 0.3925 0.6006 0.9884 1.1494     0 0.0586
## inconsistent_codriver 1.1867 0.8427 3.1116 4.7608 0.9180 1.3326     0 0.0506
## sex_driver            1.0632 0.9406 1.0531 1.6113 0.9698 1.1939     0 0.0564
## terrible_codriver     1.1337 0.8820 2.2291 3.4106 0.9392 1.2731     0 0.0529
##                         IND2
## incongruent_errors    0.2573
## inconsistent_codriver 1.7591
## sex_driver            0.6645
## terrible_codriver     1.3191
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## inconsistent_codriver , coefficient(s) are non-significant may be due to multicollinearity
## 
## R-square of y on all x: 0.3242 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================

##                       vars  n mean  sd median trimmed  mad   min  max range
## incongruent_errors       1 54 0.00 1.0  -0.35   -0.15 0.83 -0.91 3.01  3.92
## inconsistent_codriver    2 54 0.00 1.0  -0.05   -0.07 1.24 -1.39 2.49  3.89
## sex_driver*              3 54 1.54 0.5   2.00    1.55 0.00  1.00 2.00  1.00
## terrible_codriver        4 54 0.00 1.0  -0.15   -0.15 0.73 -1.37 3.60  4.98
##                        skew kurtosis   se
## incongruent_errors     1.12     0.39 0.14
## inconsistent_codriver  0.56    -0.54 0.14
## sex_driver*           -0.14    -2.02 0.07
## terrible_codriver      1.57     2.88 0.14
##                       vars  n mean  sd median trimmed  mad   min  max range
## incongruent_errors       1 54 0.00 1.0  -0.35   -0.15 0.83 -0.91 3.01  3.92
## inconsistent_codriver    2 54 0.00 1.0  -0.05   -0.07 1.24 -1.39 2.49  3.89
## sex_driver*              3 54 1.54 0.5   2.00    1.55 0.00  1.00 2.00  1.00
## terrible_codriver        4 54 0.00 1.0  -0.15   -0.15 0.73 -1.37 3.60  4.98
##                        skew kurtosis   se
## incongruent_errors     1.12     0.39 0.14
## inconsistent_codriver  0.56    -0.54 0.14
## sex_driver*           -0.14    -2.02 0.07
## terrible_codriver      1.57     2.88 0.14

## [[1]]
## 
## Call:
## lm(formula = fm, data = var_std)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -135.25  -50.64  -14.13   39.45  297.26 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                        194.463     19.239  10.108 1.78e-13 ***
## inconsistent_codriver               12.283     18.281   0.672  0.50487    
## sex_driver1                        -54.542     26.366  -2.069  0.04399 *  
## incongruent_errors                  41.178     13.052   3.155  0.00277 ** 
## terrible_codriver                   28.344     13.721   2.066  0.04428 *  
## inconsistent_codriver:sex_driver1   -2.736     26.749  -0.102  0.91895    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 93.69 on 48 degrees of freedom
## Multiple R-squared:  0.3244, Adjusted R-squared:  0.254 
## F-statistic: 4.609 on 5 and 48 DF,  p-value: 0.001629
## 
## 
## [[2]]
## 
## Call:
## lm(formula = fm, data = var_std)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -133.87  -55.76  -14.62   39.86  294.80 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                     196.38      18.93  10.371 7.57e-14 ***
## terrible_codriver                15.83      18.57   0.852  0.39826    
## sex_driver1                     -54.51      26.10  -2.088  0.04208 *  
## incongruent_errors               42.78      12.98   3.296  0.00185 ** 
## inconsistent_codriver            12.02      13.91   0.864  0.39198    
## terrible_codriver:sex_driver1    25.80      25.99   0.993  0.32585    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 92.75 on 48 degrees of freedom
## Multiple R-squared:  0.3378, Adjusted R-squared:  0.2689 
## F-statistic: 4.898 on 5 and 48 DF,  p-value: 0.001061
## 
## 
## [[3]]
## NULL

Speed overall

Using proportion of females

## [1] "DV = speed_overall"
## 
## Call:
## lm(formula = fm, data = var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0103 -0.6989  0.0295  0.7637  3.0783 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    8.3420     0.1657  50.336  < 2e-16 ***
## scale(agreeableness)          -0.1427     0.1948  -0.733  0.46739    
## scale(helpful_exchange)       -0.4041     0.1989  -2.032  0.04785 *  
## scale(inconsistent_codriver)  -0.1542     0.1808  -0.853  0.39815    
## scale(neuroticism_drone)       0.1797     0.1776   1.012  0.31694    
## scale(prop_female)            -0.5493     0.1735  -3.165  0.00272 ** 
## scale(switch_time_drone)       0.3961     0.1734   2.284  0.02693 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.218 on 47 degrees of freedom
## Multiple R-squared:  0.4417, Adjusted R-squared:  0.3704 
## F-statistic: 6.198 on 6 and 47 DF,  p-value: 7.598e-05
## 
##                       zero_order partial  part
## agreeableness              -0.33   -0.11 -0.08
## helpful_exchange           -0.40   -0.28 -0.22
## inconsistent_codriver      -0.27   -0.12 -0.09
## neuroticism_drone           0.27    0.15  0.11
## prop_female                -0.47   -0.42 -0.34
## switch_time_drone           0.31    0.32  0.25

## 
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.5590         0
## Farrar Chi-Square:        29.1814         1
## Red Indicator:             0.2071         0
## Sum of Lambda Inverse:     7.2164         0
## Theil's Method:           -1.2559         0
## Condition Number:         23.7627         0
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                          VIF    TOL     Wi     Fi Leamer   CVIF Klein   IND1
## agreeableness         1.3560 0.7374 3.4179 4.3614 0.8587 2.7864     0 0.0768
## helpful_exchange      1.4135 0.7074 3.9699 5.0658 0.8411 2.9045     0 0.0737
## inconsistent_codriver 1.1683 0.8560 1.6154 2.0613 0.9252 2.4006     0 0.0892
## neuroticism_drone     1.1277 0.8868 1.2257 1.5641 0.9417 2.3171     0 0.0924
## prop_female           1.0762 0.9292 0.7312 0.9330 0.9640 2.2113     0 0.0968
## switch_time_drone     1.0747 0.9305 0.7171 0.9151 0.9646 2.2083     0 0.0969
##                         IND2
## agreeableness         1.6536
## helpful_exchange      1.8426
## inconsistent_codriver 0.9072
## neuroticism_drone     0.7131
## prop_female           0.4458
## switch_time_drone     0.4378
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## agreeableness , inconsistent_codriver , neuroticism_drone , coefficient(s) are non-significant may be due to multicollinearity
## 
## R-square of y on all x: 0.4417 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================

##                       vars  n mean sd median trimmed  mad   min  max range
## agreeableness            1 54    0  1   0.03    0.06 0.88 -2.33 1.60  3.94
## helpful_exchange         2 54    0  1   0.04   -0.03 1.10 -1.81 2.41  4.22
## inconsistent_codriver    3 54    0  1  -0.05   -0.07 1.24 -1.39 2.49  3.89
## neuroticism_drone        4 54    0  1  -0.03   -0.03 1.03 -2.11 2.39  4.50
## prop_female              5 54    0  1  -0.43    0.10 2.15 -1.88 1.02  2.90
## switch_time_drone        6 54    0  1  -0.26   -0.08 0.94 -1.52 2.40  3.92
##                        skew kurtosis   se
## agreeableness         -0.39    -0.19 0.14
## helpful_exchange       0.20    -0.64 0.14
## inconsistent_codriver  0.56    -0.54 0.14
## neuroticism_drone      0.34    -0.56 0.14
## prop_female           -0.44    -0.91 0.14
## switch_time_drone      0.66    -0.57 0.14
##                       vars  n mean sd median trimmed  mad   min  max range
## agreeableness            1 54    0  1   0.03    0.06 0.88 -2.33 1.60  3.94
## helpful_exchange         2 54    0  1   0.04   -0.03 1.10 -1.81 2.41  4.22
## inconsistent_codriver    3 54    0  1  -0.05   -0.07 1.24 -1.39 2.49  3.89
## neuroticism_drone        4 54    0  1  -0.03   -0.03 1.03 -2.11 2.39  4.50
## prop_female              5 54    0  1  -0.43    0.10 2.15 -1.88 1.02  2.90
## switch_time_drone        6 54    0  1  -0.26   -0.08 0.94 -1.52 2.40  3.92
##                        skew kurtosis   se
## agreeableness         -0.39    -0.19 0.14
## helpful_exchange       0.20    -0.64 0.14
## inconsistent_codriver  0.56    -0.54 0.14
## neuroticism_drone      0.34    -0.56 0.14
## prop_female           -0.44    -0.91 0.14
## switch_time_drone      0.66    -0.57 0.14

## [[1]]
## 
## Call:
## lm(formula = fm, data = var_std)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.99365 -0.67991  0.03463  0.76709  3.07569 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                        8.33963    0.16891  49.374  < 2e-16 ***
## inconsistent_codriver             -0.15697    0.18448  -0.851  0.39925    
## prop_female                       -0.55078    0.17592  -3.131  0.00303 ** 
## agreeableness                     -0.14332    0.19695  -0.728  0.47050    
## helpful_exchange                  -0.40457    0.20106  -2.012  0.05007 .  
## neuroticism_drone                  0.18333    0.18253   1.004  0.32046    
## switch_time_drone                  0.39728    0.17559   2.263  0.02843 *  
## inconsistent_codriver:prop_female  0.01954    0.17689   0.110  0.91251    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.231 on 46 degrees of freedom
## Multiple R-squared:  0.4419, Adjusted R-squared:  0.3569 
## F-statistic: 5.203 on 7 and 46 DF,  p-value: 0.0002031
## 
## 
## [[2]]
## NULL
## 
## [[3]]
## 
## Call:
## lm(formula = fm, data = var_std)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0108 -0.6706  0.0411  0.7627  3.1151 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   8.33688    0.16993  49.061  < 2e-16 ***
## helpful_exchange             -0.40841    0.20241  -2.018  0.04947 *  
## prop_female                  -0.55348    0.17693  -3.128  0.00305 ** 
## agreeableness                -0.14074    0.19715  -0.714  0.47893    
## inconsistent_codriver        -0.15784    0.18385  -0.859  0.39505    
## neuroticism_drone             0.17975    0.17950   1.001  0.32188    
## switch_time_drone             0.39984    0.17648   2.266  0.02822 *  
## helpful_exchange:prop_female  0.03525    0.19732   0.179  0.85899    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.231 on 46 degrees of freedom
## Multiple R-squared:  0.4421, Adjusted R-squared:  0.3572 
## F-statistic: 5.208 on 7 and 46 DF,  p-value: 0.0002014

Using sex of driver

## [1] "DV = speed_overall"
##                       vars  n mean  sd median trimmed  mad   min  max range
## agreeableness            1 54 0.00 1.0   0.03    0.06 0.88 -2.33 1.60  3.94
## helpful_exchange         2 54 0.00 1.0   0.04   -0.03 1.10 -1.81 2.41  4.22
## inconsistent_codriver    3 54 0.00 1.0  -0.05   -0.07 1.24 -1.39 2.49  3.89
## neuroticism_drone        4 54 0.00 1.0  -0.03   -0.03 1.03 -2.11 2.39  4.50
## sex_driver*              5 54 1.54 0.5   2.00    1.55 0.00  1.00 2.00  1.00
## switch_time_drone        6 54 0.00 1.0  -0.26   -0.08 0.94 -1.52 2.40  3.92
##                        skew kurtosis   se
## agreeableness         -0.39    -0.19 0.14
## helpful_exchange       0.20    -0.64 0.14
## inconsistent_codriver  0.56    -0.54 0.14
## neuroticism_drone      0.34    -0.56 0.14
## sex_driver*           -0.14    -2.02 0.07
## switch_time_drone      0.66    -0.57 0.14
## 
## Call:
## lm(formula = fm, data = std_var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9776 -0.6537  0.1604  0.7864  3.2286 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    7.6573     0.2532  30.239  < 2e-16 ***
## scale(agreeableness)          -0.1070     0.1922  -0.557 0.580370    
## scale(helpful_exchange)       -0.3681     0.1956  -1.882 0.066101 .  
## scale(inconsistent_codriver)  -0.1128     0.1780  -0.634 0.529376    
## scale(neuroticism_drone)       0.1267     0.1743   0.727 0.470842    
## scale(switch_time_drone)       0.3038     0.1749   1.737 0.088979 .  
## sex_driver1                    1.2749     0.3619   3.523 0.000961 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.193 on 47 degrees of freedom
## Multiple R-squared:  0.4642, Adjusted R-squared:  0.3958 
## F-statistic: 6.787 on 6 and 47 DF,  p-value: 3.162e-05
## 
##                       zero_order partial  part
## agreeableness              -0.33   -0.08 -0.06
## helpful_exchange           -0.40   -0.26 -0.20
## inconsistent_codriver      -0.27   -0.09 -0.07
## neuroticism_drone           0.27    0.11  0.08
## sex_driver                  0.58    0.46  0.38
## switch_time_drone           0.31    0.25  0.19

## 
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.4870         0
## Farrar Chi-Square:        36.0934         1
## Red Indicator:             0.2352         0
## Sum of Lambda Inverse:     7.4858         0
## Theil's Method:           -1.1689         0
## Condition Number:         23.6078         0
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                          VIF    TOL     Wi     Fi Leamer   CVIF Klein   IND1
## agreeableness         1.3752 0.7271 3.6023 4.5966 0.8527 4.7394     0 0.0757
## helpful_exchange      1.4248 0.7019 4.0780 5.2037 0.8378 4.9102     0 0.0731
## inconsistent_codriver 1.1798 0.8476 1.7257 2.2020 0.9207 4.0657     0 0.0883
## neuroticism_drone     1.1318 0.8835 1.2656 1.6149 0.9400 3.9006     0 0.0920
## sex_driver            1.2351 0.8096 2.2573 2.8805 0.8998 4.2566     0 0.0843
## switch_time_drone     1.1390 0.8779 1.3347 1.7032 0.9370 3.9254     0 0.0915
##                         IND2
## agreeableness         1.4208
## helpful_exchange      1.5525
## inconsistent_codriver 0.7934
## neuroticism_drone     0.6065
## sex_driver            0.9913
## switch_time_drone     0.6356
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## agreeableness , helpful_exchange , inconsistent_codriver , neuroticism_drone , switch_time_drone , coefficient(s) are non-significant may be due to multicollinearity
## 
## R-square of y on all x: 0.4642 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================

##                       vars  n mean  sd median trimmed  mad   min  max range
## agreeableness            1 54 0.00 1.0   0.03    0.06 0.88 -2.33 1.60  3.94
## helpful_exchange         2 54 0.00 1.0   0.04   -0.03 1.10 -1.81 2.41  4.22
## inconsistent_codriver    3 54 0.00 1.0  -0.05   -0.07 1.24 -1.39 2.49  3.89
## neuroticism_drone        4 54 0.00 1.0  -0.03   -0.03 1.03 -2.11 2.39  4.50
## sex_driver*              5 54 1.54 0.5   2.00    1.55 0.00  1.00 2.00  1.00
## switch_time_drone        6 54 0.00 1.0  -0.26   -0.08 0.94 -1.52 2.40  3.92
##                        skew kurtosis   se
## agreeableness         -0.39    -0.19 0.14
## helpful_exchange       0.20    -0.64 0.14
## inconsistent_codriver  0.56    -0.54 0.14
## neuroticism_drone      0.34    -0.56 0.14
## sex_driver*           -0.14    -2.02 0.07
## switch_time_drone      0.66    -0.57 0.14
##                       vars  n mean  sd median trimmed  mad   min  max range
## agreeableness            1 54 0.00 1.0   0.03    0.06 0.88 -2.33 1.60  3.94
## helpful_exchange         2 54 0.00 1.0   0.04   -0.03 1.10 -1.81 2.41  4.22
## inconsistent_codriver    3 54 0.00 1.0  -0.05   -0.07 1.24 -1.39 2.49  3.89
## neuroticism_drone        4 54 0.00 1.0  -0.03   -0.03 1.03 -2.11 2.39  4.50
## sex_driver*              5 54 1.54 0.5   2.00    1.55 0.00  1.00 2.00  1.00
## switch_time_drone        6 54 0.00 1.0  -0.26   -0.08 0.94 -1.52 2.40  3.92
##                        skew kurtosis   se
## agreeableness         -0.39    -0.19 0.14
## helpful_exchange       0.20    -0.64 0.14
## inconsistent_codriver  0.56    -0.54 0.14
## neuroticism_drone      0.34    -0.56 0.14
## sex_driver*           -0.14    -2.02 0.07
## switch_time_drone      0.66    -0.57 0.14

## [[1]]
## 
## Call:
## lm(formula = fm, data = var_std)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0537 -0.6172  0.1482  0.6895  3.2554 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                         7.6723     0.2566  29.905  < 2e-16 ***
## inconsistent_codriver              -0.1936     0.2315  -0.836 0.407463    
## sex_driver1                         1.2857     0.3651   3.522 0.000981 ***
## agreeableness                      -0.1164     0.1944  -0.599 0.552068    
## helpful_exchange                   -0.3680     0.1971  -1.867 0.068253 .  
## neuroticism_drone                   0.1090     0.1786   0.611 0.544524    
## switch_time_drone                   0.2921     0.1775   1.646 0.106631    
## inconsistent_codriver:sex_driver1   0.1950     0.3536   0.552 0.583915    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.202 on 46 degrees of freedom
## Multiple R-squared:  0.4677, Adjusted R-squared:  0.3868 
## F-statistic: 5.775 on 7 and 46 DF,  p-value: 7.755e-05
## 
## 
## [[2]]
## NULL
## 
## [[3]]
## 
## Call:
## lm(formula = fm, data = var_std)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9771 -0.6717  0.1817  0.7957  3.2030 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   7.66313    0.25907  29.579  < 2e-16 ***
## helpful_exchange             -0.39420    0.26914  -1.465  0.14981    
## sex_driver1                   1.27540    0.36571   3.487  0.00108 ** 
## agreeableness                -0.11029    0.19558  -0.564  0.57554    
## inconsistent_codriver        -0.11051    0.18059  -0.612  0.54359    
## neuroticism_drone             0.12575    0.17633   0.713  0.47934    
## switch_time_drone             0.29914    0.17967   1.665  0.10273    
## helpful_exchange:sex_driver1  0.05049    0.35255   0.143  0.88676    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.206 on 46 degrees of freedom
## Multiple R-squared:  0.4645, Adjusted R-squared:  0.383 
## F-statistic: 5.699 on 7 and 46 DF,  p-value: 8.791e-05

Distance overall

Neither proportion of females or sex of driver correlate with distance so they were not included.

## [1] "DV = distance_overall"
## 
## Call:
## lm(formula = fm, data = var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2490.5  -793.7  -221.9   547.3  3811.0 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   12642.0      185.8  68.031  < 2e-16 ***
## scale(incongruent_errors)       386.7      203.6   1.899  0.06347 .  
## scale(inconsistent_codriver)    179.3      200.7   0.893  0.37600    
## scale(switch_cost)             -415.6      203.3  -2.044  0.04630 *  
## scale(terrible_codriver)        581.7      199.7   2.913  0.00538 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1366 on 49 degrees of freedom
## Multiple R-squared:  0.3389, Adjusted R-squared:  0.285 
## F-statistic: 6.281 on 4 and 49 DF,  p-value: 0.000367
## 
##                       zero_order partial  part
## incongruent_errors          0.34    0.26  0.22
## inconsistent_codriver       0.27    0.13  0.10
## switch_cost                -0.34   -0.28 -0.24
## terrible_codriver           0.38    0.38  0.34

## 
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.7455         0
## Farrar Chi-Square:        14.9315         1
## Red Indicator:             0.2142         0
## Sum of Lambda Inverse:     4.6313         0
## Theil's Method:           -0.4725         0
## Condition Number:          3.5825         0
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                          VIF    TOL     Wi     Fi Leamer   CVIF Klein   IND1
## incongruent_errors    1.1787 0.8484 2.9776 4.5557 0.9211 1.4361     0 0.0509
## inconsistent_codriver 1.1447 0.8736 2.4110 3.6888 0.9347 1.3947     0 0.0524
## switch_cost           1.1746 0.8514 2.9094 4.4513 0.9227 1.4312     0 0.0511
## terrible_codriver     1.1334 0.8823 2.2240 3.4027 0.9393 1.3811     0 0.0529
##                         IND2
## incongruent_errors    1.1139
## inconsistent_codriver 0.9287
## switch_cost           1.0922
## terrible_codriver     0.8652
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## incongruent_errors , inconsistent_codriver , coefficient(s) are non-significant may be due to multicollinearity
## 
## R-square of y on all x: 0.3389 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================

Which variables sig. correlate with the driving-simulation metrics during fog-free periods?

rowname collisions_no_fog_overall incongruent_errors 0.32 inconsistent_codriver 0.30 prop_female 0.33 sex_co_driver -0.28 terrible_codriver 0.36

rowname speed_no_fog_overall helpful_exchange -0.27 resilience 0.41 sex_driver 0.32 switch_time_drone 0.30

rowname distance_no_fog_overall incongruent_errors 0.34 inconsistent_codriver 0.31 switch_cost -0.31 terrible_codriver 0.39

Collisions fog-free periods

Using proportion of females

## [1] "DV = collisions_no_fog_overall"
## 
## Call:
## lm(formula = fm, data = var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -84.412 -31.182  -5.025  19.845 146.867 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   100.019      7.452  13.421  < 2e-16 ***
## scale(incongruent_errors)      21.872      7.592   2.881  0.00587 ** 
## scale(inconsistent_codriver)    8.573      8.078   1.061  0.29378    
## scale(prop_female)             19.866      7.627   2.605  0.01215 *  
## scale(terrible_codriver)       18.855      8.021   2.351  0.02281 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 54.76 on 49 degrees of freedom
## Multiple R-squared:  0.3534, Adjusted R-squared:  0.3006 
## F-statistic: 6.695 on 4 and 49 DF,  p-value: 0.0002216
## 
##                       zero_order partial part
## incongruent_errors          0.32    0.38 0.33
## inconsistent_codriver       0.30    0.15 0.12
## prop_female                 0.33    0.35 0.30
## terrible_codriver           0.36    0.32 0.27

## 
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.8516         0
## Farrar Chi-Square:         8.1628         0
## Red Indicator:             0.1618         0
## Sum of Lambda Inverse:     4.3369         0
## Theil's Method:           -0.7613         0
## Condition Number:          4.9025         0
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                          VIF    TOL     Wi     Fi Leamer   CVIF Klein   IND1
## incongruent_errors    1.0185 0.9818 0.3085 0.4720 0.9909 1.1726     0 0.0589
## inconsistent_codriver 1.1533 0.8671 2.5545 3.9084 0.9312 1.3277     0 0.0520
## prop_female           1.0281 0.9726 0.4688 0.7173 0.9862 1.1837     0 0.0584
## terrible_codriver     1.1370 0.8795 2.2830 3.4931 0.9378 1.3090     0 0.0528
##                         IND2
## incongruent_errors    0.2432
## inconsistent_codriver 1.7785
## prop_female           0.3661
## terrible_codriver     1.6122
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## inconsistent_codriver , coefficient(s) are non-significant may be due to multicollinearity
## 
## R-square of y on all x: 0.3534 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================

##                       vars  n mean sd median trimmed  mad   min  max range
## incongruent_errors       1 54    0  1  -0.35   -0.15 0.83 -0.91 3.01  3.92
## inconsistent_codriver    2 54    0  1  -0.05   -0.07 1.24 -1.39 2.49  3.89
## prop_female              3 54    0  1  -0.43    0.10 2.15 -1.88 1.02  2.90
## terrible_codriver        4 54    0  1  -0.15   -0.15 0.73 -1.37 3.60  4.98
##                        skew kurtosis   se
## incongruent_errors     1.12     0.39 0.14
## inconsistent_codriver  0.56    -0.54 0.14
## prop_female           -0.44    -0.91 0.14
## terrible_codriver      1.57     2.88 0.14
##                       vars  n mean sd median trimmed  mad   min  max range
## incongruent_errors       1 54    0  1  -0.35   -0.15 0.83 -0.91 3.01  3.92
## inconsistent_codriver    2 54    0  1  -0.05   -0.07 1.24 -1.39 2.49  3.89
## prop_female              3 54    0  1  -0.43    0.10 2.15 -1.88 1.02  2.90
## terrible_codriver        4 54    0  1  -0.15   -0.15 0.73 -1.37 3.60  4.98
##                        skew kurtosis   se
## incongruent_errors     1.12     0.39 0.14
## inconsistent_codriver  0.56    -0.54 0.14
## prop_female           -0.44    -0.91 0.14
## terrible_codriver      1.57     2.88 0.14

## [[1]]
## 
## Call:
## lm(formula = fm, data = var_std)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -84.587 -30.220  -6.167  19.030 147.411 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                         99.805      7.586  13.156  < 2e-16 ***
## inconsistent_codriver                8.301      8.248   1.006  0.31927    
## prop_female                         19.693      7.741   2.544  0.01424 *  
## incongruent_errors                  21.781      7.677   2.837  0.00665 ** 
## terrible_codriver                   18.790      8.105   2.318  0.02474 *  
## inconsistent_codriver:prop_female    1.733      7.770   0.223  0.82442    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 55.3 on 48 degrees of freedom
## Multiple R-squared:  0.3541, Adjusted R-squared:  0.2868 
## F-statistic: 5.262 on 5 and 48 DF,  p-value: 0.000623
## 
## 
## [[2]]
## 
## Call:
## lm(formula = fm, data = var_std)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -83.594 -34.164  -4.719  22.533 142.684 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    100.617      7.597  13.245  < 2e-16 ***
## terrible_codriver               20.199      8.486   2.380  0.02132 *  
## prop_female                     19.788      7.686   2.574  0.01318 *  
## incongruent_errors              22.169      7.670   2.890  0.00576 ** 
## inconsistent_codriver            8.624      8.140   1.060  0.29466    
## terrible_codriver:prop_female   -5.165      9.950  -0.519  0.60608    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 55.18 on 48 degrees of freedom
## Multiple R-squared:  0.357,  Adjusted R-squared:   0.29 
## F-statistic:  5.33 on 5 and 48 DF,  p-value: 0.0005646
## 
## 
## [[3]]
## NULL

Using sex of the codriver

## [1] "DV = collisions_no_fog_overall"
##                       vars  n mean   sd median trimmed  mad   min  max range
## incongruent_errors       1 54 0.00 1.00  -0.35   -0.15 0.83 -0.91 3.01  3.92
## inconsistent_codriver    2 54 0.00 1.00  -0.05   -0.07 1.24 -1.39 2.49  3.89
## sex_co_driver*           3 54 1.17 0.38   1.00    1.09 0.00  1.00 2.00  1.00
## terrible_codriver        4 54 0.00 1.00  -0.15   -0.15 0.73 -1.37 3.60  4.98
##                       skew kurtosis   se
## incongruent_errors    1.12     0.39 0.14
## inconsistent_codriver 0.56    -0.54 0.14
## sex_co_driver*        1.74     1.05 0.05
## terrible_codriver     1.57     2.88 0.14
## 
## Call:
## lm(formula = fm, data = std_var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -90.962 -27.590  -6.176  28.065 137.566 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   108.084      8.250  13.101   <2e-16 ***
## scale(incongruent_errors)      20.441      7.647   2.673   0.0102 *  
## scale(inconsistent_codriver)   12.148      8.142   1.492   0.1421    
## scale(terrible_codriver)       19.173      8.094   2.369   0.0218 *  
## sex_co_driver1                -48.390     20.275  -2.387   0.0209 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 55.31 on 49 degrees of freedom
## Multiple R-squared:  0.3405, Adjusted R-squared:  0.2867 
## F-statistic: 6.326 on 4 and 49 DF,  p-value: 0.0003472
## 
##                       zero_order partial  part
## incongruent_errors          0.32    0.36  0.31
## inconsistent_codriver       0.30    0.21  0.17
## sex_co_driver              -0.28   -0.32 -0.28
## terrible_codriver           0.36    0.32  0.27

## 
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.8687         0
## Farrar Chi-Square:         7.1541         0
## Red Indicator:             0.1466         0
## Sum of Lambda Inverse:     4.3050         0
## Theil's Method:           -0.7522         0
## Condition Number:          2.5474         0
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                          VIF    TOL     Wi     Fi Leamer   CVIF Klein   IND1
## incongruent_errors    1.0132 0.9870 0.2193 0.3355 0.9935 1.1219     0 0.0592
## inconsistent_codriver 1.1486 0.8706 2.4763 3.7887 0.9331 1.2719     0 0.0522
## sex_co_driver         1.0079 0.9921 0.1322 0.2022 0.9961 1.1161     0 0.0595
## terrible_codriver     1.1353 0.8808 2.2549 3.4501 0.9385 1.2571     0 0.0528
##                         IND2
## incongruent_errors    0.1928
## inconsistent_codriver 1.9208
## sex_co_driver         0.1168
## terrible_codriver     1.7696
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## inconsistent_codriver , coefficient(s) are non-significant may be due to multicollinearity
## 
## R-square of y on all x: 0.3405 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================

##                       vars  n mean   sd median trimmed  mad   min  max range
## incongruent_errors       1 54 0.00 1.00  -0.35   -0.15 0.83 -0.91 3.01  3.92
## inconsistent_codriver    2 54 0.00 1.00  -0.05   -0.07 1.24 -1.39 2.49  3.89
## sex_co_driver*           3 54 1.17 0.38   1.00    1.09 0.00  1.00 2.00  1.00
## terrible_codriver        4 54 0.00 1.00  -0.15   -0.15 0.73 -1.37 3.60  4.98
##                       skew kurtosis   se
## incongruent_errors    1.12     0.39 0.14
## inconsistent_codriver 0.56    -0.54 0.14
## sex_co_driver*        1.74     1.05 0.05
## terrible_codriver     1.57     2.88 0.14
##                       vars  n mean   sd median trimmed  mad   min  max range
## incongruent_errors       1 54 0.00 1.00  -0.35   -0.15 0.83 -0.91 3.01  3.92
## inconsistent_codriver    2 54 0.00 1.00  -0.05   -0.07 1.24 -1.39 2.49  3.89
## sex_co_driver*           3 54 1.17 0.38   1.00    1.09 0.00  1.00 2.00  1.00
## terrible_codriver        4 54 0.00 1.00  -0.15   -0.15 0.73 -1.37 3.60  4.98
##                       skew kurtosis   se
## incongruent_errors    1.12     0.39 0.14
## inconsistent_codriver 0.56    -0.54 0.14
## sex_co_driver*        1.74     1.05 0.05
## terrible_codriver     1.57     2.88 0.14

## [[1]]
## 
## Call:
## lm(formula = fm, data = var_std)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -91.698 -29.135  -9.277  28.037 134.839 
## 
## Coefficients:
##                                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                           108.139      8.295  13.036   <2e-16 ***
## inconsistent_codriver                  14.314      8.773   1.632   0.1093    
## sex_co_driver1                        -46.828     20.512  -2.283   0.0269 *  
## incongruent_errors                     20.496      7.689   2.666   0.0104 *  
## terrible_codriver                      19.140      8.139   2.352   0.0228 *  
## inconsistent_codriver:sex_co_driver1  -14.924     21.748  -0.686   0.4959    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 55.61 on 48 degrees of freedom
## Multiple R-squared:  0.3469, Adjusted R-squared:  0.2789 
## F-statistic:   5.1 on 5 and 48 DF,  p-value: 0.0007886
## 
## 
## [[2]]
## 
## Call:
## lm(formula = fm, data = var_std)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -91.701 -25.697  -8.112  27.471 134.252 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       108.094      8.228  13.137  < 2e-16 ***
## terrible_codriver                  20.597      8.172   2.520  0.01510 *  
## sex_co_driver1                    -52.540     20.557  -2.556  0.01382 *  
## incongruent_errors                 21.184      7.655   2.767  0.00801 ** 
## inconsistent_codriver              13.541      8.215   1.648  0.10580    
## terrible_codriver:sex_co_driver1  -41.566     37.054  -1.122  0.26754    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 55.16 on 48 degrees of freedom
## Multiple R-squared:  0.3574, Adjusted R-squared:  0.2904 
## F-statistic: 5.339 on 5 and 48 DF,  p-value: 0.0005573
## 
## 
## [[3]]
## NULL

Speed fog-free periods

Using sex of the driver only

## [1] "DV = speed_no_fog_overall"
##                   vars  n mean  sd median trimmed  mad   min  max range  skew
## helpful_exchange     1 54 0.00 1.0   0.04   -0.03 1.10 -1.81 2.41  4.22  0.20
## resilience           2 48 0.00 1.0  -0.13   -0.07 0.89 -2.05 3.60  5.64  0.99
## sex_driver*          3 54 1.54 0.5   2.00    1.55 0.00  1.00 2.00  1.00 -0.14
## switch_time_drone    4 54 0.00 1.0  -0.26   -0.08 0.94 -1.52 2.40  3.92  0.66
##                   kurtosis   se
## helpful_exchange     -0.64 0.14
## resilience            2.11 0.14
## sex_driver*          -2.02 0.07
## switch_time_drone    -0.57 0.14
## 
## Call:
## lm(formula = fm, data = std_var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.0810 -0.9065 -0.0349  0.8461  3.2379 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                8.7879     0.3074  28.593   <2e-16 ***
## scale(helpful_exchange)   -0.5787     0.2175  -2.660   0.0109 *  
## scale(resilience)          0.6934     0.2195   3.159   0.0029 ** 
## scale(switch_time_drone)   0.4839     0.2243   2.158   0.0366 *  
## sex_driver1                0.2630     0.4637   0.567   0.5736    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.445 on 43 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.3711, Adjusted R-squared:  0.3126 
## F-statistic: 6.344 on 4 and 43 DF,  p-value: 0.0004191
## 
##                   zero_order partial  part
## helpful_exchange       -0.30   -0.38 -0.32
## resilience              0.41    0.43  0.38
## sex_driver              0.32    0.09  0.07
## switch_time_drone       0.31    0.31  0.26

## 
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.7881         0
## Farrar Chi-Square:        10.6761         0
## Red Indicator:             0.1853         0
## Sum of Lambda Inverse:     4.5413         0
## Theil's Method:           -0.6459         0
## Condition Number:         30.5458         1
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                      VIF    TOL     Wi     Fi Leamer   CVIF Klein   IND1   IND2
## helpful_exchange  1.1282 0.8863 1.8810 2.8856 0.9415 1.2890     0 0.0604 0.9725
## resilience        1.0854 0.9213 1.2524 1.9213 0.9599 1.2400     0 0.0628 0.6731
## sex_driver        1.2345 0.8101 3.4388 5.2755 0.9000 1.4103     0 0.0552 1.6250
## switch_time_drone 1.0932 0.9147 1.3671 2.0973 0.9564 1.2489     0 0.0624 0.7295
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## sex_driver , coefficient(s) are non-significant may be due to multicollinearity
## 
## R-square of y on all x: 0.3711 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================

##                   vars  n mean  sd median trimmed  mad   min  max range  skew
## helpful_exchange     1 54 0.00 1.0   0.04   -0.03 1.10 -1.81 2.41  4.22  0.20
## resilience           2 48 0.00 1.0  -0.13   -0.07 0.89 -2.05 3.60  5.64  0.99
## sex_driver*          3 54 1.54 0.5   2.00    1.55 0.00  1.00 2.00  1.00 -0.14
## switch_time_drone    4 54 0.00 1.0  -0.26   -0.08 0.94 -1.52 2.40  3.92  0.66
##                   kurtosis   se
## helpful_exchange     -0.64 0.14
## resilience            2.11 0.14
## sex_driver*          -2.02 0.07
## switch_time_drone    -0.57 0.14

## [[1]]
## NULL
## 
## [[2]]
## NULL
## 
## [[3]]
## 
## Call:
## lm(formula = fm, data = var_std)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3.08247 -0.96516 -0.00654  0.79690  3.13847 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    8.7687     0.3166  27.696  < 2e-16 ***
## helpful_exchange              -0.5038     0.3248  -1.551  0.12843    
## sex_driver1                    0.2652     0.4687   0.566  0.57452    
## resilience                     0.6698     0.2342   2.860  0.00658 ** 
## switch_time_drone              0.5022     0.2340   2.146  0.03770 *  
## helpful_exchange:sex_driver1  -0.1456     0.4645  -0.314  0.75542    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.46 on 42 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.3726, Adjusted R-squared:  0.2979 
## F-statistic: 4.989 on 5 and 42 DF,  p-value: 0.001126

Distance fog-free periods

## [1] "DV = distance_no_fog_overall"
## 
## Call:
## lm(formula = fm, data = var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2132.6  -573.9  -158.7   457.0  2481.7 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    7738.7      126.2  61.326   <2e-16 ***
## scale(incongruent_errors)       263.4      138.3   1.905   0.0627 .  
## scale(inconsistent_codriver)    169.4      136.3   1.243   0.2197    
## scale(switch_cost)             -236.5      138.0  -1.713   0.0930 .  
## scale(terrible_codriver)        375.6      135.6   2.770   0.0079 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 927.3 on 49 degrees of freedom
## Multiple R-squared:  0.3292, Adjusted R-squared:  0.2744 
## F-statistic: 6.011 on 4 and 49 DF,  p-value: 0.0005123
## 
##                       zero_order partial  part
## incongruent_errors          0.34    0.26  0.22
## inconsistent_codriver       0.31    0.17  0.15
## switch_cost                -0.31   -0.24 -0.20
## terrible_codriver           0.39    0.37  0.32

## 
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.7455         0
## Farrar Chi-Square:        14.9315         1
## Red Indicator:             0.2142         0
## Sum of Lambda Inverse:     4.6313         0
## Theil's Method:           -0.4432         0
## Condition Number:          3.5825         0
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                          VIF    TOL     Wi     Fi Leamer   CVIF Klein   IND1
## incongruent_errors    1.1787 0.8484 2.9776 4.5557 0.9211 1.4425     0 0.0509
## inconsistent_codriver 1.1447 0.8736 2.4110 3.6888 0.9347 1.4009     0 0.0524
## switch_cost           1.1746 0.8514 2.9094 4.4513 0.9227 1.4375     0 0.0511
## terrible_codriver     1.1334 0.8823 2.2240 3.4027 0.9393 1.3872     0 0.0529
##                         IND2
## incongruent_errors    1.1139
## inconsistent_codriver 0.9287
## switch_cost           1.0922
## terrible_codriver     0.8652
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## incongruent_errors , inconsistent_codriver , switch_cost , coefficient(s) are non-significant may be due to multicollinearity
## 
## R-square of y on all x: 0.3292 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================

Which variables sig. correlate with the driving-simulation metrics during fog event probes?

rowname collisions_fog_overall incongruent_errors 0.38 sex_driver -0.28

rowname speed_fog_overall agreeableness -0.40 eng_fl_co_driver -0.30 helpful_exchange -0.44 prop_female -0.43 sex_driver 0.56

rowname distance_fog_overall switch_cost -0.31 terrible_codriver 0.28

Collisions fog event probes

Using sex of the driver only

## [1] "DV = collisions_fog_overall"
##                    vars  n mean  sd median trimmed  mad   min  max range  skew
## incongruent_errors    1 54 0.00 1.0  -0.35   -0.15 0.83 -0.91 3.01  3.92  1.12
## sex_driver*           2 54 1.54 0.5   2.00    1.55 0.00  1.00 2.00  1.00 -0.14
##                    kurtosis   se
## incongruent_errors     0.39 0.14
## sex_driver*           -2.02 0.07
## 
## Call:
## lm(formula = fm, data = std_var)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -63.18 -27.19 -11.14  15.93 167.58 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                 81.944      8.704   9.414 9.69e-13 ***
## scale(incongruent_errors)   19.834      5.987   3.313   0.0017 ** 
## sex_driver1                -30.723     11.895  -2.583   0.0127 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 43.45 on 51 degrees of freedom
## Multiple R-squared:  0.2434, Adjusted R-squared:  0.2137 
## F-statistic: 8.202 on 2 and 51 DF,  p-value: 0.000816
## 
##                    zero_order partial  part
## incongruent_errors       0.38    0.42  0.40
## sex_driver              -0.28   -0.34 -0.31

## 
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.9938         0
## Farrar Chi-Square:         0.3194         0
## Red Indicator:             0.0786         0
## Sum of Lambda Inverse:     2.0124         0
## Theil's Method:           -0.2310         0
## Condition Number:          3.1562         0
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                       VIF    TOL     Wi  Fi Leamer   CVIF Klein   IND1 IND2
## incongruent_errors 1.0062 0.9938 0.3235 Inf 0.9969 0.9823     0 0.0191    1
## sex_driver         1.0062 0.9938 0.3235 Inf 0.9969 0.9823     0 0.0191    1
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## * all coefficients have significant t-ratios
## 
## R-square of y on all x: 0.2434 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================

## [[1]]
## NULL
## 
## [[2]]
## NULL
## 
## [[3]]
## NULL

Speed fog event probes

Using proportion of females

## [1] "DV = speed_fog_overall"
## 
## Call:
## lm(formula = fm, data = var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6926 -1.2704  0.1101  0.9588  3.8091 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               8.8701     0.2159  41.083  < 2e-16 ***
## scale(agreeableness)     -0.3217     0.2568  -1.253  0.21630    
## scale(eng_fl_co_driver)  -0.1770     0.2384  -0.742  0.46151    
## scale(helpful_exchange)  -0.5407     0.2483  -2.178  0.03427 *  
## scale(prop_female)       -0.6545     0.2244  -2.917  0.00533 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.587 on 49 degrees of freedom
## Multiple R-squared:  0.3668, Adjusted R-squared:  0.3151 
## F-statistic: 7.097 on 4 and 49 DF,  p-value: 0.000137
## 
##                  zero_order partial  part
## agreeableness         -0.40   -0.18 -0.14
## eng_fl_co_driver      -0.30   -0.11 -0.08
## helpful_exchange      -0.44   -0.30 -0.25
## prop_female           -0.43   -0.38 -0.33

## 
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.6294         0
## Farrar Chi-Square:        23.5317         1
## Red Indicator:             0.2972         0
## Sum of Lambda Inverse:     4.9437         0
## Theil's Method:           -0.3697         0
## Condition Number:         19.6259         0
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                     VIF    TOL     Wi     Fi Leamer   CVIF Klein   IND1   IND2
## agreeableness    1.3885 0.7202 6.4758 9.9080 0.8486 2.3805     0 0.0432 1.5316
## eng_fl_co_driver 1.1968 0.8356 3.2800 5.0184 0.9141 2.0518     0 0.0501 0.9001
## helpful_exchange 1.2981 0.7704 4.9679 7.6009 0.8777 2.2254     0 0.0462 1.2569
## prop_female      1.0603 0.9431 1.0054 1.5382 0.9711 1.8178     0 0.0566 0.3114
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## agreeableness , eng_fl_co_driver , coefficient(s) are non-significant may be due to multicollinearity
## 
## R-square of y on all x: 0.3668 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================

##                  vars  n mean sd median trimmed  mad   min  max range  skew
## agreeableness       1 54    0  1   0.03    0.06 0.88 -2.33 1.60  3.94 -0.39
## eng_fl_co_driver    2 54    0  1   0.64    0.10 0.00 -1.53 0.64  2.17 -0.87
## helpful_exchange    3 54    0  1   0.04   -0.03 1.10 -1.81 2.41  4.22  0.20
## prop_female         4 54    0  1  -0.43    0.10 2.15 -1.88 1.02  2.90 -0.44
##                  kurtosis   se
## agreeableness       -0.19 0.14
## eng_fl_co_driver    -1.27 0.14
## helpful_exchange    -0.64 0.14
## prop_female         -0.91 0.14

## [[1]]
## NULL
## 
## [[2]]
## NULL
## 
## [[3]]
## 
## Call:
## lm(formula = fm, data = var_std)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.6915 -1.0840  0.1202  1.0398  3.6293 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    8.8966     0.2201  40.419  < 2e-16 ***
## helpful_exchange              -0.5142     0.2523  -2.038  0.04705 *  
## prop_female                   -0.6284     0.2284  -2.751  0.00836 ** 
## agreeableness                 -0.3250     0.2581  -1.259  0.21406    
## eng_fl_co_driver              -0.1881     0.2401  -0.783  0.43730    
## helpful_exchange:prop_female  -0.1813     0.2527  -0.717  0.47666    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.594 on 48 degrees of freedom
## Multiple R-squared:  0.3735, Adjusted R-squared:  0.3083 
## F-statistic: 5.724 on 5 and 48 DF,  p-value: 0.000321

Using sex of the driver

## [1] "DV = speed_fog_overall"
##                  vars  n mean  sd median trimmed  mad   min  max range  skew
## agreeableness       1 54 0.00 1.0   0.03    0.06 0.88 -2.33 1.60  3.94 -0.39
## eng_fl_co_driver    2 54 0.00 1.0   0.64    0.10 0.00 -1.53 0.64  2.17 -0.87
## helpful_exchange    3 54 0.00 1.0   0.04   -0.03 1.10 -1.81 2.41  4.22  0.20
## sex_driver*         4 54 1.54 0.5   2.00    1.55 0.00  1.00 2.00  1.00 -0.14
##                  kurtosis   se
## agreeableness       -0.19 0.14
## eng_fl_co_driver    -1.27 0.14
## helpful_exchange    -0.64 0.14
## sex_driver*         -2.02 0.07
## 
## Call:
## lm(formula = fm, data = std_var)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4794 -1.0264  0.1233  0.9248  4.0248 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               7.9576     0.3093  25.731  < 2e-16 ***
## scale(agreeableness)     -0.2513     0.2444  -1.028  0.30884    
## scale(eng_fl_co_driver)  -0.1640     0.2252  -0.728  0.47002    
## scale(helpful_exchange)  -0.4675     0.2359  -1.982  0.05315 .  
## sex_driver1               1.6992     0.4328   3.926  0.00027 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.499 on 49 degrees of freedom
## Multiple R-squared:  0.4347, Adjusted R-squared:  0.3886 
## F-statistic:  9.42 on 4 and 49 DF,  p-value: 9.93e-06
## 
##                  zero_order partial  part
## agreeableness         -0.40   -0.15 -0.11
## eng_fl_co_driver      -0.30   -0.10 -0.08
## helpful_exchange      -0.44   -0.27 -0.21
## sex_driver             0.56    0.49  0.42

## 
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.5964         0
## Farrar Chi-Square:        26.2758         1
## Red Indicator:             0.3206         0
## Sum of Lambda Inverse:     5.0361         0
## Theil's Method:           -0.5057         0
## Condition Number:         19.5565         0
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                     VIF    TOL     Wi      Fi Leamer   CVIF Klein   IND1   IND2
## agreeableness    1.4083 0.7101 6.8058 10.4129 0.8426 3.2543     0 0.0426 1.4525
## eng_fl_co_driver 1.1962 0.8360 3.2702  5.0034 0.9143 2.7641     0 0.0502 0.8217
## helpful_exchange 1.3124 0.7620 5.2070  7.9667 0.8729 3.0327     0 0.0457 1.1925
## sex_driver       1.1191 0.8935 1.9856  3.0379 0.9453 2.5860     0 0.0536 0.5333
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## agreeableness , eng_fl_co_driver , helpful_exchange , coefficient(s) are non-significant may be due to multicollinearity
## 
## R-square of y on all x: 0.4347 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================

##                  vars  n mean  sd median trimmed  mad   min  max range  skew
## agreeableness       1 54 0.00 1.0   0.03    0.06 0.88 -2.33 1.60  3.94 -0.39
## eng_fl_co_driver    2 54 0.00 1.0   0.64    0.10 0.00 -1.53 0.64  2.17 -0.87
## helpful_exchange    3 54 0.00 1.0   0.04   -0.03 1.10 -1.81 2.41  4.22  0.20
## sex_driver*         4 54 1.54 0.5   2.00    1.55 0.00  1.00 2.00  1.00 -0.14
##                  kurtosis   se
## agreeableness       -0.19 0.14
## eng_fl_co_driver    -1.27 0.14
## helpful_exchange    -0.64 0.14
## sex_driver*         -2.02 0.07

## [[1]]
## NULL
## 
## [[2]]
## NULL
## 
## [[3]]
## 
## Call:
## lm(formula = fm, data = var_std)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4722 -0.8283  0.1254  0.8589  3.7175 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    8.0491     0.3132  25.701  < 2e-16 ***
## helpful_exchange              -0.7712     0.3189  -2.419 0.019423 *  
## sex_driver1                    1.6610     0.4295   3.867 0.000331 ***
## agreeableness                 -0.2855     0.2432  -1.174 0.246271    
## eng_fl_co_driver              -0.1758     0.2232  -0.788 0.434819    
## helpful_exchange:sex_driver1   0.5944     0.4247   1.400 0.168054    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.485 on 48 degrees of freedom
## Multiple R-squared:  0.4569, Adjusted R-squared:  0.4003 
## F-statistic: 8.076 on 5 and 48 DF,  p-value: 1.372e-05

Distance fog event probes

## [1] "DV = distance_fog_overall"
## 
## Call:
## lm(formula = fm, data = var)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1248.60  -348.79   -70.83   238.25  1699.10 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               4903.35      89.23  54.949   <2e-16 ***
## scale(switch_cost)        -226.92      90.15  -2.517   0.0150 *  
## scale(terrible_codriver)   209.84      90.15   2.328   0.0239 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 655.7 on 51 degrees of freedom
## Multiple R-squared:  0.1812, Adjusted R-squared:  0.1491 
## F-statistic: 5.642 on 2 and 51 DF,  p-value: 0.006115
## 
##                   zero_order partial  part
## switch_cost            -0.31   -0.33 -0.32
## terrible_codriver       0.28    0.31  0.29

## 
## Call:
## omcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## Overall Multicollinearity Diagnostics
## 
##                        MC Results detection
## Determinant |X'X|:         0.9983         0
## Farrar Chi-Square:         0.0899         0
## Red Indicator:             0.0418         0
## Sum of Lambda Inverse:     2.0035         0
## Theil's Method:           -0.1777         0
## Condition Number:          2.2462         0
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## 
## Call:
## imcdiag(x = vars %>% select(names(var)), y = vars %>% select(dv))
## 
## 
## All Individual Multicollinearity Diagnostics Result
## 
##                      VIF    TOL     Wi  Fi Leamer   CVIF Klein   IND1 IND2
## switch_cost       1.0017 0.9983 0.0908 Inf 0.9991 0.9926     0 0.0192    1
## terrible_codriver 1.0017 0.9983 0.0908 Inf 0.9991 0.9926     0 0.0192    1
## 
## 1 --> COLLINEARITY is detected by the test 
## 0 --> COLLINEARITY is not detected by the test
## 
## * all coefficients have significant t-ratios
## 
## R-square of y on all x: 0.1812 
## 
## * use method argument to check which regressors may be the reason of collinearity
## ===================================